Binarization & Thresholding Methods

BinarizerImage component is a subpart of the Image Transformer component.

Binarization converts an image to black and white, making every pixel either 0 or 255. Any color or grayscale image can be binarized. Methods fall into two types:

• Global: applies a single threshold to all pixels.
• Local: applies varying thresholds per pixel, usually performing better on images with uneven lighting.

Otary offers 5 basic and 12 advanced binarization methods.

Basic methods: simple, otsu, adaptive, bradley, and sauvola are available directly from the Image object:

import otary as ot

im = ot.Image.from_file(filepath="path/to/file/image")
im.threshold_sauvola()

Advanced methods are accessible via the transformer.binarizer attribute:

im.transformer.binarizer.threshold_isauvola()

BinarizerImage

BinarizerImage class contains all the binarization methods.

It includes only two global thresholding methods: threshold_simple and threshold_otsu. The other methods are local thresholding methods.

It includes the following binarization methods, sorted by year of publication:

Name	Year	Reference
Adaptive	-	OpenCV Adaptive Thresholding Documentation
Otsu	1979	A Threshold Selection Method from Gray-Level Histograms
Bernsen	1986	"Dynamic thresholding of grey-level images" by Bernsen
Niblack	1986	"An Introduction to Digital Image Processing" by Wayne Niblack
Sauvola	1997	Adaptive Document Binarization
Wolf	2003	Extraction and Recognition of Artificial Text in Multimedia Documents
Feng	2004	Contrast adaptive binarization of low quality document images
Gatos	2005	Adaptive degraded document image binarization
Bradley & Roth	2007	Adaptive Thresholding using the Integral Image
Nick	2009	Comparison of Niblack inspired Binarization Methods for Ancient Documents
Su	2010	Binarization of historical document images using the local maximum and minimum
Phansalkar	2011	Adaptive Local Thresholding for Detection of Nuclei in Diversely Stained Cytology Images
Adotsu	2011	AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization
Singh	2012	A New Local Adaptive Thresholding Technique in Binarization
FAIR	2013	FAIR: A Fast Algorithm for document Image Restoration
ISauvola	2016	ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization
WAN	2018	Binarization of Document Image Using Optimum Threshold Modification

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

class BinarizerImage:
    # pylint: disable=line-too-long
    """BinarizerImage class contains all the binarization methods.

    It includes only two global thresholding methods: `threshold_simple` and `threshold_otsu`. The other methods are local thresholding methods.

    It includes the following binarization methods, sorted by year of publication:

    | Name           | Year | Reference                                                                                                                        |
    |----------------|------|------------------------------------------------------------------------------------------------------------------------------------------|
    | Adaptive       |  -   | [OpenCV Adaptive Thresholding Documentation](https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html)                           |
    | Otsu           | 1979 | [A Threshold Selection Method from Gray-Level Histograms](https://ieeexplore.ieee.org/document/4310076)                                  |
    | Bernsen        | 1986 | "Dynamic thresholding of grey-level images" by Bernsen                                                                                   |
    | Niblack        | 1986 | "An Introduction to Digital Image Processing" by Wayne Niblack                                                                           |
    | Sauvola        | 1997 | [Adaptive Document Binarization](https://www.researchgate.net/publication/3710586_Adaptive_Document_Binarization)                        |
    | Wolf           | 2003 | [Extraction and Recognition of Artificial Text in Multimedia Documents](https://hal.science/hal-01504401v1)                              |
    | Feng           | 2004 | [Contrast adaptive binarization of low quality document images](https://www.jstage.jst.go.jp/article/elex/1/16/1_16_501/_pdf)            |
    | Gatos          | 2005 | [Adaptive degraded document image binarization](https://users.iit.demokritos.gr/~bgat/PatRec2006.pdf)                                    |
    | Bradley & Roth | 2007 | [Adaptive Thresholding using the Integral Image](https://www.researchgate.net/publication/220494200_Adaptive_Thresholding_using_the_Integral_Image) |
    | Nick           | 2009 | [Comparison of Niblack inspired Binarization Methods for Ancient Documents](https://www.researchgate.net/publication/221253803)           |
    | Su             | 2010 | [Binarization of historical document images using the local maximum and minimum](https://www.researchgate.net/publication/220933012)                                                             |
    | Phansalkar     | 2011 | [Adaptive Local Thresholding for Detection of Nuclei in Diversely Stained Cytology Images](https://www.researchgate.net/publication/224226466) |
    | Adotsu         | 2011 | [AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization](https://www.researchgate.net/publication/220602345) |
    | Singh          | 2012 | [A New Local Adaptive Thresholding Technique in Binarization](https://www.researchgate.net/publication/220485031)                        |
    | FAIR           | 2013 | [FAIR: A Fast Algorithm for document Image Restoration](https://amu.hal.science/hal-01479805/document)                                   |
    | ISauvola       | 2016 | [ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization](https://www.researchgate.net/publication/304621554_ISauvola_Improved_Sauvola) |
    | WAN            | 2018 | [Binarization of Document Image Using Optimum Threshold Modification](https://www.researchgate.net/publication/326026836)                 |
    """

    def __init__(self, base: BaseImage) -> None:
        self.base = base

    # ----------------------------- GLOBAL THRESHOLDING -------------------------------

    def threshold_simple(self, thresh: int) -> "Image":
        """Compute the image thresholded by a single value T.
        All pixels with value v <= T are turned black and those with value v > T are
        turned white. This is a global thresholding method.

        Args:
            thresh (int): value to separate the black from the white pixels.
        """
        self.base.as_grayscale()
        self.base.asarray = np.array((self.base.asarray > thresh) * 255, dtype=np.uint8)
        return self.base.parent

    def threshold_otsu(self) -> "Image":
        """Apply Otsu global thresholding.
        This is a global thresholding method that automatically determines
        an optimal threshold value from the image histogram.

        Paper (1979):
        [A Threshold Selection Method from Gray-Level Histograms](https://ieeexplore.ieee.org/document/4310076)

        Consider applying a gaussian blur before for better thresholding results.
        See why in the [OpenCV documentation](https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html).
        """
        self.base.as_grayscale()
        _, img_thresholded = cv2.threshold(
            self.base.asarray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
        )
        self.base.asarray = img_thresholded
        return self.base.parent

    # ------------------------------ LOCAL THRESHOLDING -------------------------------

    def threshold_adaptive(
        self, block_size: int = 11, constant: float = 2.0
    ) -> "Image":
        """Apply adaptive local thresholding.
        This is a local thresholding method that computes the threshold for a pixel
        based on a small region around it.

        Consider applying a gaussian blur before for better thresholding results.
        See why in the [OpenCV documentation](https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html).

        Args:
            block_size (int, optional): Size of a pixel neighborhood that is used to
                calculate a threshold value for the pixel: 3, 5, 7, and so on.
                Defaults to 11.
            constant (int, optional): Constant subtracted from the mean or weighted
                mean. Normally, it is positive but may be zero or negative as well.
                Defaults to 2.
        """
        self.base.as_grayscale()
        self.base.asarray = cv2.adaptiveThreshold(
            src=self.base.asarray,
            maxValue=255,
            adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
            thresholdType=cv2.THRESH_BINARY,
            blockSize=block_size,
            C=constant,
        )
        return self.base.parent

    def threshold_bernsen(
        self,
        window_size: int = 75,
        contrast_limit: float = 25,
        threshold_global: int = 100,
    ) -> "Image":
        """Apply Bernsen local thresholding.

        Paper (1986):
        "Dynamic thresholding of grey-level images" by Bernsen.

        Args:
            window_size (int, optional): window size for local computations.
                Defaults to 75.
            contrast_limit (float, optional): contrast limit. If the
                contrast is higher than this value, the pixel is thresholded by the
                bernsen threshold otherwise the global threshold is used.
                Defaults to 25.
            threshold_global (int, optional): global threshold. Defaults to 100.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_bernsen(
            img=self.base.asarray,
            window_size=window_size,
            contrast_limit=contrast_limit,
            threshold_global=threshold_global,
        )
        return self.base.parent

    def threshold_niblack(self, window_size: int = 15, k: float = -0.2) -> "Image":
        """Apply Niblack local thresholding.

        Book (1986):
        "An Introduction to Digital Image Processing" by Wayne Niblack.

        Args:
            window_size (int, optional): apply on the
                image. Defaults to 15.
            k (float, optional): factor to apply to regulate the impact
                of the std. Defaults to -0.2.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_niblack_like(
            img=self.base.asarray, method="niblack", window_size=window_size, k=k
        )[1]
        return self.base.parent

    def threshold_sauvola(
        self, window_size: int = 15, k: float = 0.5, r: float = 128.0
    ) -> "Image":
        """Apply Sauvola local thresholding.
        This is a local thresholding method that computes the threshold for a pixel
        based on a small region around it.

        Paper (1997):
        [Adaptive Document Binarization](https://www.researchgate.net/publication/3710586_Adaptive_Document_Binarization)

        Args:
            window_size (int, optional): sauvola window size to apply on the
                image. Defaults to 15.
            k (float, optional): sauvola k factor to apply to regulate the impact
                of the std. Defaults to 0.5.
            r (float, optional): sauvola r value. Defaults to 128.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_niblack_like(
            img=self.base.asarray, method="sauvola", window_size=window_size, k=k, r=r
        )[1]
        return self.base.parent

    def threshold_wolf(self, window_size: int = 15, k: float = 0.5) -> "Image":
        """Apply Wolf local thresholding.

        Paper (2003):
        [Extraction and Recognition of Artificial Text in Multimedia Documents](https://hal.science/hal-01504401v1)

        Args:
            window_size (int, optional): apply on the
                image. Defaults to 15.
            k (float, optional): factor to apply to regulate the impact
                of the std. Defaults to 0.5.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_niblack_like(
            img=self.base.asarray, method="wolf", window_size=window_size, k=k
        )[1]
        return self.base.parent

    def threshold_feng(
        self,
        w1: int = 19,
        w2: int = 33,
        alpha1: float = 0.12,
        k1: float = 0.25,
        k2: float = 0.04,
        gamma: float = 2.0,
    ) -> "Image":
        """Implementation of the Feng thresholding method.

        Paper (2004):
        [Contrast adaptive binarization of low quality document images](https://www.jstage.jst.go.jp/article/elex/1/16/1_16_501/_pdf)

        Args:
            w1 (int, optional): primary window size. Defaults to 19.
            w2 (int, optional): secondary window value. Defaults to 33.
            alpha1 (float, optional): alpha1 value. Defaults to 0.12.
            k1 (float, optional): k1 value. Defaults to 0.25.
            k2 (float, optional): k2 value. Defaults to 0.04.
            gamma (float, optional): gamma value. Defaults to 2.0.
        """
        # pylint: disable=too-many-arguments, too-many-positional-arguments
        self.base.as_grayscale()
        self.base.asarray = threshold_feng(
            img=self.base.asarray,
            w1=w1,
            w2=w2,
            alpha1=alpha1,
            k1=k1,
            k2=k2,
            gamma=gamma,
        )
        return self.base.parent

    def threshold_gatos(
        self,
        q: float = 0.6,
        p1: float = 0.5,
        p2: float = 0.8,
        lh: Optional[float] = None,
        upsampling: bool = False,
        upsampling_factor: int = 2,
    ) -> "Image":
        """Apply Gatos local thresholding.

        Paper (2005):
        [Adaptive degraded document image binarization](https://users.iit.demokritos.gr/~bgat/PatRec2006.pdf)

        Args:
            q (float, optional): q gatos factor. Defaults to 0.6.
            p1 (float, optional): p1 gatos factor. Defaults to 0.5.
            p2 (float, optional): p2 gatos factor. Defaults to 0.8.
            lh (Optional[float], optional): height of character.
                Defaults to None, meaning it is computed automatically to be
                a fraction of the image size.
            upsampling (bool, optional): whether to apply gatos upsampling definition.
                Defaults to False.
            upsampling_factor (int, optional): gatos upsampling factor. Defaults to 2.
        """
        # pylint: disable=too-many-arguments, too-many-positional-arguments
        # pylint: disable=duplicate-code
        self.base.as_grayscale()
        self.base.asarray = threshold_gatos(
            img=self.base.asarray,
            q=q,
            p1=p1,
            p2=p2,
            lh=lh,
            upsampling=upsampling,
            upsampling_factor=upsampling_factor,
        )
        return self.base.parent

    def threshold_bradley(self, window_size: int = 15, t: float = 0.15) -> "Image":
        """Implementation of the Bradley & Roth thresholding method.

        Paper (2007):
        [Adaptive Thresholding using the Integral Image](https://www.researchgate.net/publication/220494200_Adaptive_Thresholding_using_the_Integral_Image)

        Args:
            window_size (int, optional): window size for local computations.
                Defaults to 15.
            t (float, optional): t value in [0, 1]. Defaults to 0.15.

        Returns:
            NDArray[np.uint8]: output thresholded image
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_bradley(
            img=self.base.asarray, window_size=window_size, t=t
        )
        return self.base.parent

    def threshold_nick(self, window_size: int = 19, k: float = -0.1) -> "Image":
        """Apply Nick local thresholding.

        Paper (2009):
        [Comparison of Niblack inspired Binarization Methods for Ancient Documents](https://www.researchgate.net/publication/221253803)

        The paper suggests to use a window size of 19 and a k factor in [-0.2, -0.1].

        Args:
            window_size (int, optional): apply on the
                image. Defaults to 15.
            k (float, optional): factor to apply to regulate the impact
                of the std. Defaults to -0.1.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_niblack_like(
            img=self.base.asarray, method="nick", window_size=window_size, k=k
        )[1]
        return self.base.parent

    def threshold_su(
        self,
        window_size: int = 3,
        n_min: int = -1,
    ) -> "Image":
        """Compute the Su local thresholding.

        Paper (2010):
        [Binarization of historical document images using the local maximum and minimum](https://www.researchgate.net/publication/220933012)

        Args:
            window_size (int, optional): window size for high contrast image
                computation. Defaults to 3.
            n_min (int, optional): minimum number of high contrast pixels within the
                neighborhood window. Defaults to -1 meaning that n_min = window_size.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_su(
            img=self.base.asarray, window_size=window_size, n_min=n_min
        )
        return self.base.parent

    def threshold_phansalkar(
        self, window_size: int = 40, k: float = 0.25, p: float = 3.0, q: float = 10.0
    ) -> "Image":
        """Apply Phansalkar et al. local thresholding.

        Paper (2011):
        [Adaptive Local Thresholding for Detection of Nuclei in Diversely Stained Cytology Images](https://www.researchgate.net/publication/224226466)

        Args:
            window_size (int, optional): apply on the
                image. Defaults to 40.
            k (float, optional): factor to apply to regulate the impact
                of the std. Defaults to 0.25.
            p (float, optional): Phansalkar parameter to regulate low contrast zones.
                Defaults to 3.0.
            q (float, optional): Phansalkar parameter to regulate low contrast zones.
                Defaults to 10.0.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_niblack_like(
            img=self.base.asarray,
            method="phansalkar",
            window_size=window_size,
            k=k,
            p=p,
            q=q,
        )[1]
        return self.base.parent

    def threshold_adotsu(
        self, grid_size: int = 50, k_sigma: float = 1.6, n_steps: int = 2
    ) -> "Image":
        """Apply Adotsu local thresholding.

        Paper (2011):
        [AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization](https://www.researchgate.net/publication/220602345)

        Args:
            grid_size (int, optional): window size for local computations.
                Defaults to 15.
            k_sigma (float, optional): k_sigma value in [1, 2]. Defaults to 1.6.
            n_steps (int, optional): number of iterations to update the binarization by
                estimating a new background surface. Defaults to 2.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_adotsu(
            img=self.base.asarray, grid_size=grid_size, k_sigma=k_sigma, n_steps=n_steps
        )
        return self.base.parent

    def threshold_singh(self, window_size: int = 15, k: float = 0.06) -> "Image":
        """Apply Singh local thresholding.

        Paper (2012):
        [A New Local Adaptive Thresholding Technique in Binarization](https://www.researchgate.net/publication/220485031)

        Args:
            window_size (int, optional): apply on the
                image. Defaults to 15.
            k (float, optional): factor to apply to regulate the impact
                of the std. Defaults to 0.06.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_niblack_like(
            img=self.base.asarray, method="singh", window_size=window_size, k=k
        )[1]
        return self.base.parent

    def threshold_fair(
        self,
        sfair_window_size: int = 33,
        sfair_clustering_algo: str = "otsu",
        sfair_clustering_max_iter: int = 20,
        sfair_thining: float = 1.0,
        sfair_alpha: float = 0.38,
        post_stain_max_pixels: int = 25,
        post_misclass_txt: bool = True,
        post_clustering_algo: str = "otsu",
        post_clustering_max_iter: int = 10,
        post_max_iter: int = 15,
        post_window_size: int = 75,
        post_beta: float = 1.0,
    ) -> "Image":
        """Apply FAIR local thresholding.

        Paper (2013):
        [FAIR: A Fast Algorithm for document Image Restoration](https://amu.hal.science/hal-01479805/document)

        Args:
            sfair_window_size (int, optional): window size in preprocess
                to cluster background and foreground pixels around edge pixels.
                This parameter is important as a higher value will make the method
                more robust to noise but also more computationally expensive and slow.
                Defaults to 5.
            sfair_clustering_algo (str, optional): clustering algorithm for the S-FAIR
                step. Defaults to "otsu".
            sfair_clustering_max_iter (int, optional): maximum number of iterations for
                the clustering algorithm within the S-FAIR step. Defaults to 20.
            sfair_thining (float, optional): thining factor in [0, 1]. 0 means no
                thinning which means that all edge pixels are processed.
                1 means that only every
                sfair_window_size // 2 edge pixels are processed which signicantly
                speeds up the computation. Defaults to 1.0.
            sfair_alpha (float, optional): It defines the ratio to compute the lower
                threshold in the 1st step of the S-FAIR step.
                It is generally in [0.3, 0.5].
                Defaults to 0.38.
            post_stain_max_pixels (int, optional): maximum number of pixels for a stain
                to be considered as an unknown connected component. Defaults to 25.
            post_misclass_txt (bool, optional): whether to perform the
                post-processing correct_misclassified_text_pixels step.
                Defaults to True.
            post_clustering_algo (str, optional): clustering algorithm for the
                post-processing step. Defaults to "otsu".
            post_clustering_max_iter (int, optional): maximum number of iterations for
                the clustering algorithm within the post-processing step.
                Defaults to 10.
            post_max_iter (int, optional): maximum number of iterations for the
                correct_misclassified_text_pixels step within the post-processing step.
                Defaults to 15.
            post_window_size (int, optional): window size in postprocess
                to cluster background and foreground pixels around edge pixels.
                This parameter is important as a higher value will make the method
                more robust to noise but also more computationally expensive and slow.
                Defaults to 75.
            post_beta (float, optional): factor to define if the unkown pixels
                should be set as text or background. If beta is 1 then
                unknown pixels are set to text if the number of surrounding text pixels
                (N_t) is higher than the number of surrounding background pixels (N_b).
                Simply N_t > N_b. Beta is the value to put more flexibility on the rule
                and thus set unknown pixels to text if N_t > beta * N_b
                Defaults to 1.0.
        """
        # pylint: disable=too-many-arguments, too-many-positional-arguments
        # pylint: disable=duplicate-code
        self.base.as_grayscale()
        self.base.asarray = threshold_fair(
            img=self.base.asarray,
            sfair_window_size=sfair_window_size,
            sfair_clustering_algo=sfair_clustering_algo,
            sfair_clustering_max_iter=sfair_clustering_max_iter,
            sfair_thining=sfair_thining,
            sfair_alpha=sfair_alpha,
            post_stain_max_pixels=post_stain_max_pixels,
            post_misclass_txt=post_misclass_txt,
            post_clustering_algo=post_clustering_algo,
            post_max_iter=post_max_iter,
            post_clustering_max_iter=post_clustering_max_iter,
            post_window_size=post_window_size,
            post_beta=post_beta,
        )
        return self.base.parent

    def threshold_isauvola(
        self,
        window_size: int = 15,
        k: float = 0.01,
        r: float = 128.0,
        connectivity: int = 8,
        contrast_window_size: int = 3,
        opening_n_min_pixels: int = 0,
        opening_connectivity: int = 8,
    ) -> "Image":
        """Apply ISauvola local thresholding.

        Paper (2016):
        [ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization](https://www.researchgate.net/publication/304621554_ISauvola_Improved_Sauvola)

        Args:
            window_size (int, optional): apply on the
                image. Defaults to 15.
            k (float, optional): factor to apply to regulate the impact
                of the std. Defaults to 0.01.
            r (float, optional): factor to apply to regulate the impact
                of the std. Defaults to 128.
            connectivity (int, optional): connectivity to apply on the
                image. Defaults to 8.
            contrast_window_size (int, optional): contrast window size to apply on the
                image. Defaults to 3.
            opening_n_min_pixels (int, optional): opening n min pixels to apply on the
                image. Defaults to 0.
            opening_connectivity (int, optional): opening connectivity to apply on the
                image. Defaults to 8.
        """
        # pylint: disable=too-many-arguments, too-many-positional-arguments
        self.base.as_grayscale()
        self.base.asarray = threshold_isauvola(
            img=self.base.asarray,
            window_size=window_size,
            k=k,
            r=r,
            connectivity=connectivity,
            contrast_window_size=contrast_window_size,
            opening_n_min_pixels=opening_n_min_pixels,
            opening_connectivity=opening_connectivity,
        )
        return self.base.parent

    def threshold_wan(
        self, window_size: int = 15, k: float = 0.5, r: float = 128.0
    ) -> "Image":
        """Apply Wan local thresholding.

        Paper (2018):
        [Binarization of Document Image Using Optimum Threshold Modification](https://www.researchgate.net/publication/326026836)

        Args:
            window_size (int, optional): apply on the
                image. Defaults to 15.
            k (float, optional): factor to apply to regulate the impact
                of the std. Defaults to 0.5.
        """
        self.base.as_grayscale()
        self.base.asarray = threshold_niblack_like(
            img=self.base.asarray, method="wan", window_size=window_size, k=k, r=r
        )[1]
        return self.base.parent

    # ---------------------------- BINARY REPRESENTATION ------------------------------

    def binary(self, method: BinarizationMethods = "sauvola") -> NDArray:
        """Binary representation of the image with values that can be only 0 or 1.
        The value 0 is now 0 and value of 255 are now 1. Black is 0 and white is 1.
        We can also talk about the mask of the image to refer to the binary
        representation of it.

        The sauvola is generally the best binarization method however it is
        way slower than the others methods. The adaptative or otsu method are the best
        method in terms of speed and quality.

        Args:
            method (str, optional): the binarization method to apply.
                Look at the BinarizationMethods to see all the available methods.
                Defaults to "sauvola".

        Returns:
            NDArray: array where its inner values are 0 or 1
        """
        if method not in list(get_args(BinarizationMethods)):
            raise ValueError(
                f"Invalid binarization method {method}. "
                f"Must be in {BinarizationMethods}"
            )
        getattr(self, f"threshold_{method}")()
        return self.base.asarray_binary

    def binaryrev(self, method: BinarizationMethods = "sauvola") -> NDArray:
        """Reversed binary representation of the image.
        The value 0 is now 1 and value of 255 are now 0. Black is 1 and white is 0.
        This is why it is called the "binary rev" or "binary reversed".

        Args:
            method (str, optional): the binarization method to apply.
                Defaults to "adaptative".

        Returns:
            NDArray: array where its inner values are 0 or 1
        """
        return 1 - self.binary(method=method)

binary(method='sauvola')

Binary representation of the image with values that can be only 0 or 1. The value 0 is now 0 and value of 255 are now 1. Black is 0 and white is 1. We can also talk about the mask of the image to refer to the binary representation of it.

The sauvola is generally the best binarization method however it is way slower than the others methods. The adaptative or otsu method are the best method in terms of speed and quality.

Parameters:

Name	Type	Description	Default
method	str	the binarization method to apply. Look at the BinarizationMethods to see all the available methods. Defaults to "sauvola".	'sauvola'

Returns:

Name	Type	Description
NDArray	NDArray	array where its inner values are 0 or 1

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def binary(self, method: BinarizationMethods = "sauvola") -> NDArray:
    """Binary representation of the image with values that can be only 0 or 1.
    The value 0 is now 0 and value of 255 are now 1. Black is 0 and white is 1.
    We can also talk about the mask of the image to refer to the binary
    representation of it.

    The sauvola is generally the best binarization method however it is
    way slower than the others methods. The adaptative or otsu method are the best
    method in terms of speed and quality.

    Args:
        method (str, optional): the binarization method to apply.
            Look at the BinarizationMethods to see all the available methods.
            Defaults to "sauvola".

    Returns:
        NDArray: array where its inner values are 0 or 1
    """
    if method not in list(get_args(BinarizationMethods)):
        raise ValueError(
            f"Invalid binarization method {method}. "
            f"Must be in {BinarizationMethods}"
        )
    getattr(self, f"threshold_{method}")()
    return self.base.asarray_binary

binaryrev(method='sauvola')

Reversed binary representation of the image. The value 0 is now 1 and value of 255 are now 0. Black is 1 and white is 0. This is why it is called the "binary rev" or "binary reversed".

Parameters:

Name	Type	Description	Default
method	str	the binarization method to apply. Defaults to "adaptative".	'sauvola'

Returns:

Name	Type	Description
NDArray	NDArray	array where its inner values are 0 or 1

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def binaryrev(self, method: BinarizationMethods = "sauvola") -> NDArray:
    """Reversed binary representation of the image.
    The value 0 is now 1 and value of 255 are now 0. Black is 1 and white is 0.
    This is why it is called the "binary rev" or "binary reversed".

    Args:
        method (str, optional): the binarization method to apply.
            Defaults to "adaptative".

    Returns:
        NDArray: array where its inner values are 0 or 1
    """
    return 1 - self.binary(method=method)

threshold_adaptive(block_size=11, constant=2.0)

Apply adaptive local thresholding. This is a local thresholding method that computes the threshold for a pixel based on a small region around it.

Consider applying a gaussian blur before for better thresholding results. See why in the OpenCV documentation.

Parameters:

Name	Type	Description	Default
block_size	int	Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. Defaults to 11.	11
constant	int	Constant subtracted from the mean or weighted mean. Normally, it is positive but may be zero or negative as well. Defaults to 2.	2.0

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_adaptive(
    self, block_size: int = 11, constant: float = 2.0
) -> "Image":
    """Apply adaptive local thresholding.
    This is a local thresholding method that computes the threshold for a pixel
    based on a small region around it.

    Consider applying a gaussian blur before for better thresholding results.
    See why in the [OpenCV documentation](https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html).

    Args:
        block_size (int, optional): Size of a pixel neighborhood that is used to
            calculate a threshold value for the pixel: 3, 5, 7, and so on.
            Defaults to 11.
        constant (int, optional): Constant subtracted from the mean or weighted
            mean. Normally, it is positive but may be zero or negative as well.
            Defaults to 2.
    """
    self.base.as_grayscale()
    self.base.asarray = cv2.adaptiveThreshold(
        src=self.base.asarray,
        maxValue=255,
        adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        thresholdType=cv2.THRESH_BINARY,
        blockSize=block_size,
        C=constant,
    )
    return self.base.parent

threshold_adotsu(grid_size=50, k_sigma=1.6, n_steps=2)

Apply Adotsu local thresholding.

Paper (2011): AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization

Parameters:

Name	Type	Description	Default
grid_size	int	window size for local computations. Defaults to 15.	50
k_sigma	float	k_sigma value in [1, 2]. Defaults to 1.6.	1.6
n_steps	int	number of iterations to update the binarization by estimating a new background surface. Defaults to 2.	2

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_adotsu(
    self, grid_size: int = 50, k_sigma: float = 1.6, n_steps: int = 2
) -> "Image":
    """Apply Adotsu local thresholding.

    Paper (2011):
    [AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization](https://www.researchgate.net/publication/220602345)

    Args:
        grid_size (int, optional): window size for local computations.
            Defaults to 15.
        k_sigma (float, optional): k_sigma value in [1, 2]. Defaults to 1.6.
        n_steps (int, optional): number of iterations to update the binarization by
            estimating a new background surface. Defaults to 2.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_adotsu(
        img=self.base.asarray, grid_size=grid_size, k_sigma=k_sigma, n_steps=n_steps
    )
    return self.base.parent

threshold_bernsen(window_size=75, contrast_limit=25, threshold_global=100)

Apply Bernsen local thresholding.

Paper (1986): "Dynamic thresholding of grey-level images" by Bernsen.

Parameters:

Name	Type	Description	Default
window_size	int	window size for local computations. Defaults to 75.	75
contrast_limit	float	contrast limit. If the contrast is higher than this value, the pixel is thresholded by the bernsen threshold otherwise the global threshold is used. Defaults to 25.	25
threshold_global	int	global threshold. Defaults to 100.	100

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_bernsen(
    self,
    window_size: int = 75,
    contrast_limit: float = 25,
    threshold_global: int = 100,
) -> "Image":
    """Apply Bernsen local thresholding.

    Paper (1986):
    "Dynamic thresholding of grey-level images" by Bernsen.

    Args:
        window_size (int, optional): window size for local computations.
            Defaults to 75.
        contrast_limit (float, optional): contrast limit. If the
            contrast is higher than this value, the pixel is thresholded by the
            bernsen threshold otherwise the global threshold is used.
            Defaults to 25.
        threshold_global (int, optional): global threshold. Defaults to 100.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_bernsen(
        img=self.base.asarray,
        window_size=window_size,
        contrast_limit=contrast_limit,
        threshold_global=threshold_global,
    )
    return self.base.parent

threshold_bradley(window_size=15, t=0.15)

Implementation of the Bradley & Roth thresholding method.

Paper (2007): Adaptive Thresholding using the Integral Image

Parameters:

Name	Type	Description	Default
window_size	int	window size for local computations. Defaults to 15.	15
t	float	t value in [0, 1]. Defaults to 0.15.	0.15

Returns:

Type	Description
Image	NDArray[np.uint8]: output thresholded image

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_bradley(self, window_size: int = 15, t: float = 0.15) -> "Image":
    """Implementation of the Bradley & Roth thresholding method.

    Paper (2007):
    [Adaptive Thresholding using the Integral Image](https://www.researchgate.net/publication/220494200_Adaptive_Thresholding_using_the_Integral_Image)

    Args:
        window_size (int, optional): window size for local computations.
            Defaults to 15.
        t (float, optional): t value in [0, 1]. Defaults to 0.15.

    Returns:
        NDArray[np.uint8]: output thresholded image
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_bradley(
        img=self.base.asarray, window_size=window_size, t=t
    )
    return self.base.parent

threshold_fair(sfair_window_size=33, sfair_clustering_algo='otsu', sfair_clustering_max_iter=20, sfair_thining=1.0, sfair_alpha=0.38, post_stain_max_pixels=25, post_misclass_txt=True, post_clustering_algo='otsu', post_clustering_max_iter=10, post_max_iter=15, post_window_size=75, post_beta=1.0)

Apply FAIR local thresholding.

Paper (2013): FAIR: A Fast Algorithm for document Image Restoration

Parameters:

Name	Type	Description	Default
sfair_window_size	int	window size in preprocess to cluster background and foreground pixels around edge pixels. This parameter is important as a higher value will make the method more robust to noise but also more computationally expensive and slow. Defaults to 5.	33
sfair_clustering_algo	str	clustering algorithm for the S-FAIR step. Defaults to "otsu".	'otsu'
sfair_clustering_max_iter	int	maximum number of iterations for the clustering algorithm within the S-FAIR step. Defaults to 20.	20
sfair_thining	float	thining factor in [0, 1]. 0 means no thinning which means that all edge pixels are processed. 1 means that only every sfair_window_size // 2 edge pixels are processed which signicantly speeds up the computation. Defaults to 1.0.	1.0
sfair_alpha	float	It defines the ratio to compute the lower threshold in the 1st step of the S-FAIR step. It is generally in [0.3, 0.5]. Defaults to 0.38.	0.38
post_stain_max_pixels	int	maximum number of pixels for a stain to be considered as an unknown connected component. Defaults to 25.	25
post_misclass_txt	bool	whether to perform the post-processing correct_misclassified_text_pixels step. Defaults to True.	True
post_clustering_algo	str	clustering algorithm for the post-processing step. Defaults to "otsu".	'otsu'
post_clustering_max_iter	int	maximum number of iterations for the clustering algorithm within the post-processing step. Defaults to 10.	10
post_max_iter	int	maximum number of iterations for the correct_misclassified_text_pixels step within the post-processing step. Defaults to 15.	15
post_window_size	int	window size in postprocess to cluster background and foreground pixels around edge pixels. This parameter is important as a higher value will make the method more robust to noise but also more computationally expensive and slow. Defaults to 75.	75
post_beta	float	factor to define if the unkown pixels should be set as text or background. If beta is 1 then unknown pixels are set to text if the number of surrounding text pixels (N_t) is higher than the number of surrounding background pixels (N_b). Simply N_t > N_b. Beta is the value to put more flexibility on the rule and thus set unknown pixels to text if N_t > beta * N_b Defaults to 1.0.	1.0

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_fair(
    self,
    sfair_window_size: int = 33,
    sfair_clustering_algo: str = "otsu",
    sfair_clustering_max_iter: int = 20,
    sfair_thining: float = 1.0,
    sfair_alpha: float = 0.38,
    post_stain_max_pixels: int = 25,
    post_misclass_txt: bool = True,
    post_clustering_algo: str = "otsu",
    post_clustering_max_iter: int = 10,
    post_max_iter: int = 15,
    post_window_size: int = 75,
    post_beta: float = 1.0,
) -> "Image":
    """Apply FAIR local thresholding.

    Paper (2013):
    [FAIR: A Fast Algorithm for document Image Restoration](https://amu.hal.science/hal-01479805/document)

    Args:
        sfair_window_size (int, optional): window size in preprocess
            to cluster background and foreground pixels around edge pixels.
            This parameter is important as a higher value will make the method
            more robust to noise but also more computationally expensive and slow.
            Defaults to 5.
        sfair_clustering_algo (str, optional): clustering algorithm for the S-FAIR
            step. Defaults to "otsu".
        sfair_clustering_max_iter (int, optional): maximum number of iterations for
            the clustering algorithm within the S-FAIR step. Defaults to 20.
        sfair_thining (float, optional): thining factor in [0, 1]. 0 means no
            thinning which means that all edge pixels are processed.
            1 means that only every
            sfair_window_size // 2 edge pixels are processed which signicantly
            speeds up the computation. Defaults to 1.0.
        sfair_alpha (float, optional): It defines the ratio to compute the lower
            threshold in the 1st step of the S-FAIR step.
            It is generally in [0.3, 0.5].
            Defaults to 0.38.
        post_stain_max_pixels (int, optional): maximum number of pixels for a stain
            to be considered as an unknown connected component. Defaults to 25.
        post_misclass_txt (bool, optional): whether to perform the
            post-processing correct_misclassified_text_pixels step.
            Defaults to True.
        post_clustering_algo (str, optional): clustering algorithm for the
            post-processing step. Defaults to "otsu".
        post_clustering_max_iter (int, optional): maximum number of iterations for
            the clustering algorithm within the post-processing step.
            Defaults to 10.
        post_max_iter (int, optional): maximum number of iterations for the
            correct_misclassified_text_pixels step within the post-processing step.
            Defaults to 15.
        post_window_size (int, optional): window size in postprocess
            to cluster background and foreground pixels around edge pixels.
            This parameter is important as a higher value will make the method
            more robust to noise but also more computationally expensive and slow.
            Defaults to 75.
        post_beta (float, optional): factor to define if the unkown pixels
            should be set as text or background. If beta is 1 then
            unknown pixels are set to text if the number of surrounding text pixels
            (N_t) is higher than the number of surrounding background pixels (N_b).
            Simply N_t > N_b. Beta is the value to put more flexibility on the rule
            and thus set unknown pixels to text if N_t > beta * N_b
            Defaults to 1.0.
    """
    # pylint: disable=too-many-arguments, too-many-positional-arguments
    # pylint: disable=duplicate-code
    self.base.as_grayscale()
    self.base.asarray = threshold_fair(
        img=self.base.asarray,
        sfair_window_size=sfair_window_size,
        sfair_clustering_algo=sfair_clustering_algo,
        sfair_clustering_max_iter=sfair_clustering_max_iter,
        sfair_thining=sfair_thining,
        sfair_alpha=sfair_alpha,
        post_stain_max_pixels=post_stain_max_pixels,
        post_misclass_txt=post_misclass_txt,
        post_clustering_algo=post_clustering_algo,
        post_max_iter=post_max_iter,
        post_clustering_max_iter=post_clustering_max_iter,
        post_window_size=post_window_size,
        post_beta=post_beta,
    )
    return self.base.parent

threshold_feng(w1=19, w2=33, alpha1=0.12, k1=0.25, k2=0.04, gamma=2.0)

Implementation of the Feng thresholding method.

Paper (2004): Contrast adaptive binarization of low quality document images

Parameters:

Name	Type	Description	Default
w1	int	primary window size. Defaults to 19.	19
w2	int	secondary window value. Defaults to 33.	33
alpha1	float	alpha1 value. Defaults to 0.12.	0.12
k1	float	k1 value. Defaults to 0.25.	0.25
k2	float	k2 value. Defaults to 0.04.	0.04
gamma	float	gamma value. Defaults to 2.0.	2.0

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_feng(
    self,
    w1: int = 19,
    w2: int = 33,
    alpha1: float = 0.12,
    k1: float = 0.25,
    k2: float = 0.04,
    gamma: float = 2.0,
) -> "Image":
    """Implementation of the Feng thresholding method.

    Paper (2004):
    [Contrast adaptive binarization of low quality document images](https://www.jstage.jst.go.jp/article/elex/1/16/1_16_501/_pdf)

    Args:
        w1 (int, optional): primary window size. Defaults to 19.
        w2 (int, optional): secondary window value. Defaults to 33.
        alpha1 (float, optional): alpha1 value. Defaults to 0.12.
        k1 (float, optional): k1 value. Defaults to 0.25.
        k2 (float, optional): k2 value. Defaults to 0.04.
        gamma (float, optional): gamma value. Defaults to 2.0.
    """
    # pylint: disable=too-many-arguments, too-many-positional-arguments
    self.base.as_grayscale()
    self.base.asarray = threshold_feng(
        img=self.base.asarray,
        w1=w1,
        w2=w2,
        alpha1=alpha1,
        k1=k1,
        k2=k2,
        gamma=gamma,
    )
    return self.base.parent

threshold_gatos(q=0.6, p1=0.5, p2=0.8, lh=None, upsampling=False, upsampling_factor=2)

Apply Gatos local thresholding.

Paper (2005): Adaptive degraded document image binarization

Parameters:

Name	Type	Description	Default
q	float	q gatos factor. Defaults to 0.6.	0.6
p1	float	p1 gatos factor. Defaults to 0.5.	0.5
p2	float	p2 gatos factor. Defaults to 0.8.	0.8
lh	Optional[float]	height of character. Defaults to None, meaning it is computed automatically to be a fraction of the image size.	None
upsampling	bool	whether to apply gatos upsampling definition. Defaults to False.	False
upsampling_factor	int	gatos upsampling factor. Defaults to 2.	2

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_gatos(
    self,
    q: float = 0.6,
    p1: float = 0.5,
    p2: float = 0.8,
    lh: Optional[float] = None,
    upsampling: bool = False,
    upsampling_factor: int = 2,
) -> "Image":
    """Apply Gatos local thresholding.

    Paper (2005):
    [Adaptive degraded document image binarization](https://users.iit.demokritos.gr/~bgat/PatRec2006.pdf)

    Args:
        q (float, optional): q gatos factor. Defaults to 0.6.
        p1 (float, optional): p1 gatos factor. Defaults to 0.5.
        p2 (float, optional): p2 gatos factor. Defaults to 0.8.
        lh (Optional[float], optional): height of character.
            Defaults to None, meaning it is computed automatically to be
            a fraction of the image size.
        upsampling (bool, optional): whether to apply gatos upsampling definition.
            Defaults to False.
        upsampling_factor (int, optional): gatos upsampling factor. Defaults to 2.
    """
    # pylint: disable=too-many-arguments, too-many-positional-arguments
    # pylint: disable=duplicate-code
    self.base.as_grayscale()
    self.base.asarray = threshold_gatos(
        img=self.base.asarray,
        q=q,
        p1=p1,
        p2=p2,
        lh=lh,
        upsampling=upsampling,
        upsampling_factor=upsampling_factor,
    )
    return self.base.parent

threshold_isauvola(window_size=15, k=0.01, r=128.0, connectivity=8, contrast_window_size=3, opening_n_min_pixels=0, opening_connectivity=8)

Apply ISauvola local thresholding.

Paper (2016): ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

Parameters:

Name	Type	Description	Default
window_size	int	apply on the image. Defaults to 15.	15
k	float	factor to apply to regulate the impact of the std. Defaults to 0.01.	0.01
r	float	factor to apply to regulate the impact of the std. Defaults to 128.	128.0
connectivity	int	connectivity to apply on the image. Defaults to 8.	8
contrast_window_size	int	contrast window size to apply on the image. Defaults to 3.	3
opening_n_min_pixels	int	opening n min pixels to apply on the image. Defaults to 0.	0
opening_connectivity	int	opening connectivity to apply on the image. Defaults to 8.	8

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_isauvola(
    self,
    window_size: int = 15,
    k: float = 0.01,
    r: float = 128.0,
    connectivity: int = 8,
    contrast_window_size: int = 3,
    opening_n_min_pixels: int = 0,
    opening_connectivity: int = 8,
) -> "Image":
    """Apply ISauvola local thresholding.

    Paper (2016):
    [ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization](https://www.researchgate.net/publication/304621554_ISauvola_Improved_Sauvola)

    Args:
        window_size (int, optional): apply on the
            image. Defaults to 15.
        k (float, optional): factor to apply to regulate the impact
            of the std. Defaults to 0.01.
        r (float, optional): factor to apply to regulate the impact
            of the std. Defaults to 128.
        connectivity (int, optional): connectivity to apply on the
            image. Defaults to 8.
        contrast_window_size (int, optional): contrast window size to apply on the
            image. Defaults to 3.
        opening_n_min_pixels (int, optional): opening n min pixels to apply on the
            image. Defaults to 0.
        opening_connectivity (int, optional): opening connectivity to apply on the
            image. Defaults to 8.
    """
    # pylint: disable=too-many-arguments, too-many-positional-arguments
    self.base.as_grayscale()
    self.base.asarray = threshold_isauvola(
        img=self.base.asarray,
        window_size=window_size,
        k=k,
        r=r,
        connectivity=connectivity,
        contrast_window_size=contrast_window_size,
        opening_n_min_pixels=opening_n_min_pixels,
        opening_connectivity=opening_connectivity,
    )
    return self.base.parent

threshold_niblack(window_size=15, k=-0.2)

Apply Niblack local thresholding.

Book (1986): "An Introduction to Digital Image Processing" by Wayne Niblack.

Parameters:

Name	Type	Description	Default
window_size	int	apply on the image. Defaults to 15.	15
k	float	factor to apply to regulate the impact of the std. Defaults to -0.2.	-0.2

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_niblack(self, window_size: int = 15, k: float = -0.2) -> "Image":
    """Apply Niblack local thresholding.

    Book (1986):
    "An Introduction to Digital Image Processing" by Wayne Niblack.

    Args:
        window_size (int, optional): apply on the
            image. Defaults to 15.
        k (float, optional): factor to apply to regulate the impact
            of the std. Defaults to -0.2.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_niblack_like(
        img=self.base.asarray, method="niblack", window_size=window_size, k=k
    )[1]
    return self.base.parent

threshold_nick(window_size=19, k=-0.1)

Apply Nick local thresholding.

Paper (2009): Comparison of Niblack inspired Binarization Methods for Ancient Documents

The paper suggests to use a window size of 19 and a k factor in [-0.2, -0.1].

Parameters:

Name	Type	Description	Default
window_size	int	apply on the image. Defaults to 15.	19
k	float	factor to apply to regulate the impact of the std. Defaults to -0.1.	-0.1

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_nick(self, window_size: int = 19, k: float = -0.1) -> "Image":
    """Apply Nick local thresholding.

    Paper (2009):
    [Comparison of Niblack inspired Binarization Methods for Ancient Documents](https://www.researchgate.net/publication/221253803)

    The paper suggests to use a window size of 19 and a k factor in [-0.2, -0.1].

    Args:
        window_size (int, optional): apply on the
            image. Defaults to 15.
        k (float, optional): factor to apply to regulate the impact
            of the std. Defaults to -0.1.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_niblack_like(
        img=self.base.asarray, method="nick", window_size=window_size, k=k
    )[1]
    return self.base.parent

threshold_otsu()

Apply Otsu global thresholding. This is a global thresholding method that automatically determines an optimal threshold value from the image histogram.

Paper (1979): A Threshold Selection Method from Gray-Level Histograms

Consider applying a gaussian blur before for better thresholding results. See why in the OpenCV documentation.

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_otsu(self) -> "Image":
    """Apply Otsu global thresholding.
    This is a global thresholding method that automatically determines
    an optimal threshold value from the image histogram.

    Paper (1979):
    [A Threshold Selection Method from Gray-Level Histograms](https://ieeexplore.ieee.org/document/4310076)

    Consider applying a gaussian blur before for better thresholding results.
    See why in the [OpenCV documentation](https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html).
    """
    self.base.as_grayscale()
    _, img_thresholded = cv2.threshold(
        self.base.asarray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
    )
    self.base.asarray = img_thresholded
    return self.base.parent

threshold_phansalkar(window_size=40, k=0.25, p=3.0, q=10.0)

Apply Phansalkar et al. local thresholding.

Paper (2011): Adaptive Local Thresholding for Detection of Nuclei in Diversely Stained Cytology Images

Parameters:

Name	Type	Description	Default
window_size	int	apply on the image. Defaults to 40.	40
k	float	factor to apply to regulate the impact of the std. Defaults to 0.25.	0.25
p	float	Phansalkar parameter to regulate low contrast zones. Defaults to 3.0.	3.0
q	float	Phansalkar parameter to regulate low contrast zones. Defaults to 10.0.	10.0

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_phansalkar(
    self, window_size: int = 40, k: float = 0.25, p: float = 3.0, q: float = 10.0
) -> "Image":
    """Apply Phansalkar et al. local thresholding.

    Paper (2011):
    [Adaptive Local Thresholding for Detection of Nuclei in Diversely Stained Cytology Images](https://www.researchgate.net/publication/224226466)

    Args:
        window_size (int, optional): apply on the
            image. Defaults to 40.
        k (float, optional): factor to apply to regulate the impact
            of the std. Defaults to 0.25.
        p (float, optional): Phansalkar parameter to regulate low contrast zones.
            Defaults to 3.0.
        q (float, optional): Phansalkar parameter to regulate low contrast zones.
            Defaults to 10.0.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_niblack_like(
        img=self.base.asarray,
        method="phansalkar",
        window_size=window_size,
        k=k,
        p=p,
        q=q,
    )[1]
    return self.base.parent

threshold_sauvola(window_size=15, k=0.5, r=128.0)

Apply Sauvola local thresholding. This is a local thresholding method that computes the threshold for a pixel based on a small region around it.

Paper (1997): Adaptive Document Binarization

Parameters:

Name	Type	Description	Default
window_size	int	sauvola window size to apply on the image. Defaults to 15.	15
k	float	sauvola k factor to apply to regulate the impact of the std. Defaults to 0.5.	0.5
r	float	sauvola r value. Defaults to 128.	128.0

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_sauvola(
    self, window_size: int = 15, k: float = 0.5, r: float = 128.0
) -> "Image":
    """Apply Sauvola local thresholding.
    This is a local thresholding method that computes the threshold for a pixel
    based on a small region around it.

    Paper (1997):
    [Adaptive Document Binarization](https://www.researchgate.net/publication/3710586_Adaptive_Document_Binarization)

    Args:
        window_size (int, optional): sauvola window size to apply on the
            image. Defaults to 15.
        k (float, optional): sauvola k factor to apply to regulate the impact
            of the std. Defaults to 0.5.
        r (float, optional): sauvola r value. Defaults to 128.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_niblack_like(
        img=self.base.asarray, method="sauvola", window_size=window_size, k=k, r=r
    )[1]
    return self.base.parent

threshold_simple(thresh)

Compute the image thresholded by a single value T. All pixels with value v <= T are turned black and those with value v > T are turned white. This is a global thresholding method.

Parameters:

Name	Type	Description	Default
thresh	int	value to separate the black from the white pixels.	required

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_simple(self, thresh: int) -> "Image":
    """Compute the image thresholded by a single value T.
    All pixels with value v <= T are turned black and those with value v > T are
    turned white. This is a global thresholding method.

    Args:
        thresh (int): value to separate the black from the white pixels.
    """
    self.base.as_grayscale()
    self.base.asarray = np.array((self.base.asarray > thresh) * 255, dtype=np.uint8)
    return self.base.parent

threshold_singh(window_size=15, k=0.06)

Apply Singh local thresholding.

Paper (2012): A New Local Adaptive Thresholding Technique in Binarization

Parameters:

Name	Type	Description	Default
window_size	int	apply on the image. Defaults to 15.	15
k	float	factor to apply to regulate the impact of the std. Defaults to 0.06.	0.06

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_singh(self, window_size: int = 15, k: float = 0.06) -> "Image":
    """Apply Singh local thresholding.

    Paper (2012):
    [A New Local Adaptive Thresholding Technique in Binarization](https://www.researchgate.net/publication/220485031)

    Args:
        window_size (int, optional): apply on the
            image. Defaults to 15.
        k (float, optional): factor to apply to regulate the impact
            of the std. Defaults to 0.06.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_niblack_like(
        img=self.base.asarray, method="singh", window_size=window_size, k=k
    )[1]
    return self.base.parent

threshold_su(window_size=3, n_min=-1)

Compute the Su local thresholding.

Paper (2010): Binarization of historical document images using the local maximum and minimum

Parameters:

Name	Type	Description	Default
window_size	int	window size for high contrast image computation. Defaults to 3.	3
n_min	int	minimum number of high contrast pixels within the neighborhood window. Defaults to -1 meaning that n_min = window_size.	-1

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_su(
    self,
    window_size: int = 3,
    n_min: int = -1,
) -> "Image":
    """Compute the Su local thresholding.

    Paper (2010):
    [Binarization of historical document images using the local maximum and minimum](https://www.researchgate.net/publication/220933012)

    Args:
        window_size (int, optional): window size for high contrast image
            computation. Defaults to 3.
        n_min (int, optional): minimum number of high contrast pixels within the
            neighborhood window. Defaults to -1 meaning that n_min = window_size.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_su(
        img=self.base.asarray, window_size=window_size, n_min=n_min
    )
    return self.base.parent

threshold_wan(window_size=15, k=0.5, r=128.0)

Apply Wan local thresholding.

Paper (2018): Binarization of Document Image Using Optimum Threshold Modification

Parameters:

Name	Type	Description	Default
window_size	int	apply on the image. Defaults to 15.	15
k	float	factor to apply to regulate the impact of the std. Defaults to 0.5.	0.5

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_wan(
    self, window_size: int = 15, k: float = 0.5, r: float = 128.0
) -> "Image":
    """Apply Wan local thresholding.

    Paper (2018):
    [Binarization of Document Image Using Optimum Threshold Modification](https://www.researchgate.net/publication/326026836)

    Args:
        window_size (int, optional): apply on the
            image. Defaults to 15.
        k (float, optional): factor to apply to regulate the impact
            of the std. Defaults to 0.5.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_niblack_like(
        img=self.base.asarray, method="wan", window_size=window_size, k=k, r=r
    )[1]
    return self.base.parent

threshold_wolf(window_size=15, k=0.5)

Apply Wolf local thresholding.

Paper (2003): Extraction and Recognition of Artificial Text in Multimedia Documents

Parameters:

Name	Type	Description	Default
window_size	int	apply on the image. Defaults to 15.	15
k	float	factor to apply to regulate the impact of the std. Defaults to 0.5.	0.5

Source code in otary/image/components/transformer/components/binarizer/binarizer.py

def threshold_wolf(self, window_size: int = 15, k: float = 0.5) -> "Image":
    """Apply Wolf local thresholding.

    Paper (2003):
    [Extraction and Recognition of Artificial Text in Multimedia Documents](https://hal.science/hal-01504401v1)

    Args:
        window_size (int, optional): apply on the
            image. Defaults to 15.
        k (float, optional): factor to apply to regulate the impact
            of the std. Defaults to 0.5.
    """
    self.base.as_grayscale()
    self.base.asarray = threshold_niblack_like(
        img=self.base.asarray, method="wolf", window_size=window_size, k=k
    )[1]
    return self.base.parent