The present disclosure discloses a character offset detection method and system. The method includes: acquiring a text image; performing character separation based on the text image to obtain a character text region; calculating a center point of each rectangular box in the character text region to obtain a center point set; determining an optimal fitted curve based on the center point set; and analyzing character offset based on the optimal fitted curve to obtain an offset result. The present disclosure realizes detection of the character offset based on curve fitting, so that the accuracy of detection is improved.

An image processing system and an image processing method for localising recognised characters in an image. An estimation unit is configured to estimate a first location of a recognised character that has been obtained by performing character recognition of the image. A determination unit is configured to determine second locations of a plurality of connected components in the image. A comparison unit is configured to compare the first location and the second locations, to identify a connected component associated with the recognised character. An association unit is configured to associate the recognised character, the identified connected component, and the second location of the identified connected component.

An image processing system and an image processing method for localising recognised characters in an image. An estimation unit is configured to estimate a first location of a recognised character that has been obtained by performing character recognition of the image. A determination unit is configured to determine second locations of a plurality of connected components in the image. A comparison unit is configured to compare the first location and the second locations, to identify a connected component associated with the recognised character. An association unit is configured to associate the recognised character, the identified connected component, and the second location of the identified connected component.

An image processing system and an image processing method for localising recognised characters in an image. An estimation unit is configured to estimate a first location of a recognised character that has been obtained by performing character recognition of the image. A determination unit is configured to determine second locations of a plurality of connected components in the image. A comparison unit is configured to compare the first location and the second locations, to identify a connected component associated with the recognised character. An association unit is configured to associate the recognised character, the identified connected component, and the second location of the identified connected component.

Provided is an image processing apparatus that improves the performance when performing image processing accompanying an analysis. An image acquisition unit acquires processing image data for performing processing. A normal image processing unit performs normal image processing for normal output on the processing image data acquired by the image acquisition unit and outputs the normal image data. An analysis image processing unit performs analysis image processing accompanied by analysis in parallel with the normal image processing by the normal image processing unit and outputs the analysis image data. A merge processing unit merges the normal image data output by the normal image processing unit and the analysis image data output by the analysis image processing unit and outputs print data for printing.

An OCR system acquires the position of an image code in a document image, acquires data indicated by the image code, and acquires the position of a handwriting input field in the document image on the basis of the position of the image code in the acquired document image, the position of the image code in the document included in the acquired data, and the position of the handwriting input field in the document included in the acquired data.

A computer-implemented method (300) for extracting content (302) from a physical writing surface (304), the method (300) comprising the steps of:

(a) receiving a reference frame (306) including image data relating to at least a portion of the physical writing surface (304), the image data including a set of data points;

(b) determining an extraction region (308), the extraction region (308) including a subset of the set of data points from which content (302) is to be extracted;

(c) extracting content (302) from the extraction region (308) and writing the content (302) to a display frame (394);

(d) receiving a subsequent frame (406) including subsequent image data relating to at least a portion of the physical writing surface (304), the subsequent image data including a subsequent set of data points;

(e) determining a subsequent extraction region (408), the subsequent extraction region (408) including a subset of the subsequent set of data points from which content (402) is to be extracted; and

(f) extracting subsequent content (402) from the subsequent extraction region (408) and writing the subsequent content (402) to the display frame (394).

An image forming apparatus, a scanned image correction method of an image forming apparatus, and a non-transitory computer-readable recording medium are provided. The image forming apparatus includes a scan unit to scan a document to generate a scanned image and a processor to detect a skew angle of the scanned image, determine a reference point on the basis of a position of a content in the scanned image, and rotate the scanned image around the determined reference point to correct the skew angle.

Provided are character image processing methods and apparatuses, devices, storage medium, and computer programs. The character image processing method mainly comprises: obtaining at least one image block containing a character in a character image to be processed; obtaining image block form transformation information of the image block on the basis of a neural network, the image block form transformation information being used for changing a character orientation in the image block to a predetermined orientation, and the neural network being obtained by means of training using an image block sample having form transformation label information; performing form transformation processing on the character image to be processed according to the image block form transformation information; and performing character recognition on the character image to be processed which is subjected to the form transformation.

Disclosed herein are system, method, and computer program product embodiments for optical character recognition (OCR) pre-processing using machine learning. In an embodiment, a neural network may be trained to identify a standardized document rotation and scale expected by an OCR service performing character recognition. The neural network may then analyze a received document image to identify a corresponding rotation and scale of the document image relative to the expected standardized values. In response to this identification, the document image may be modified in the inverse to standardize the rotation and scale of the document image to match the format expected by the OCR service. In some embodiments, a neural network may perform the standardization as well as the character recognition using a shared computation graph.