An image processing apparatus comprises: an acquiring unit configured to acquire image data generated by reading an original; a specifying unit configured to specify an object region including a predetermined object in the image data; a removing unit configured to remove, from the object region, noise with a size smaller than a size specified by a first threshold; and a setting unit configured to set the first threshold, for each object region specified by the specifying unit, in accordance with the size of the object region.

A method and system identifies and addresses imaging artifacts in an image of a financial document to enable a software system to provide financial services based on the image of the financial document. The method and system receive document image data, extract image features from the document image data, and apply the image features to an analytics model to generate an image classification, according to one embodiment. The method and system use the image classification to determine whether the document image data contains one or more particular image artifacts (e.g., Moiré patterns), according to one embodiment. If the software system determines that it is likely that the document image data contains one or more particular image artifacts, then the software system applies a filter (e.g., a median filter) to the document image data to reduce and/or remove the one or more particular image artifacts, according to one embodiment.

A method, system, and computer program product for segmenting and processing documents for optical character recognition is provided. The method includes receiving a document and detecting different types of text data. The document is divided into a plurality of text regions associated with the different types of said text data. Optical noise is removed from each text region and differing optical character recognition software code is selected for application to each text region. The differing optical character recognition software code is executed with respect to each text region resulting in extractable computer readable text located within each said text region.

According to the present disclosure, an image-processing apparatus acquires horizontal black hat image data by taking a difference between source image data and horizontal closing image data obtained by performing closing processing on each pixel row of the source image data, acquires vertical black hat image data by taking a difference between the source image data and vertical closing image data obtained by performing the closing processing on each pixel column of the source image data, and acquires logical conjunction image data where a logical conjunction of the horizontal black hat image data and the vertical black hat image data is taken.

A processor determines a cause of an image defect based on a test image that is obtained through an image reading process performed on an output sheet output from an image forming device. The processor generates an extraction image by extracting, from the test image, a noise point that is a type of image defect. Furthermore, the processor determines a cause of the noise point by using at least one of, in the extraction image: an edge strength of the noise point; a degree of flatness of the noise point; and a pixel value distribution of a transverse pixel sequence that is a pixel sequence traversing the noise point.

An image processing apparatus that is capable of restoring a degraded image with high accuracy The image processing apparatus acquires image data including an image of character information and identifies a character type of the character information. A learned model adapted to the identified character type is acquired from a plurality of learned models subjected to machine learning using images for learning, which are associated with a plurality of character type conditions, respectively, and correct answer images associated therewith. The image of character information is input to the acquired learned model to restore the image of character information.

A method, system, and computer program product for segmenting and processing documents for optical character recognition is provided. The method includes receiving a document and detecting different types of text data. The document is divided into a plurality of text regions associated with the different types of said text data. Optical noise is removed from each text region and differing optical character recognition software code is selected for application to each text region. The differing optical character recognition software code is executed with respect to each text region resulting in extractable computer readable text located within each said text region.

A device for removing a noise artifact from a document receives a scan of the document, where the document contains a noise artifact at least partially obstructing a portion of the document. The device generates an image of the document, and extracts a first set of features from the image. The device identifies noise artifact features from the first set of features, and generates a second set of features by removing the noise artifact features. The device generates a test clean image of the document based on the second set of features. The device determines whether a portion of the test clean image that previously displayed the noise artifact corresponds to a counterpart portion of the training clean image. If it is determined that the portion of the test clean image corresponds to the counterpart portion of the training clean image, the device outputs the test clean image.

According to an exemplary embodiment of the present disclosure, an isolated dot is removed from an image including a shading dot, and a shading dot in contact with a character is removed from the isolated dot-removed image based on a size of the removed isolated dot.

An image processing method, an image processing device, an electronic device, and a storage medium are provided. The image processing method includes: obtaining an input image, wherein the input image includes M character rows; performing global correction processing on the input image to obtain an intermediate corrected image; determining the M character row lower boundaries; determining the relative offset of all pixels in the intermediate corrected image according to the M character row lower boundaries, the first image boundary and the second image boundary of the intermediate corrected image; determining the local adjustment offset of all pixels in the intermediate corrected image according to the relative offsets of all pixels in the intermediate corrected image; and performing local adjustment on the intermediate corrected image according to the local adjustment offsets of all pixels in the intermediate corrected image to obtain the target corrected image.