A method and system of extracting one or more non-semantic entities in a document image including data entities is disclosed. The methodology includes extraction, by a processor, of row entities and corresponding row location based on a text extraction technique from the document image. The row entities are split into split-row entities based on a splitting rule. Semantic entities are determined from alphabetic entities using semantic recognition technique. The non-semantic entities are determined as split-row entities other than semantic entities. Feature values of each feature type for each of the non-semantic entities is determined. The processor further determines a first probability output for non-semantic entities and a second probability output for semantic entities surrounding the non-semantic entities. The system further labels each of the non-semantic entities based on determination of a highest probability value from a sum of the first probability output and the second probability output.

In a symbol recognition device, each histogram computation module receives an image of each partial region of a recognition target region in a binarized image and computes a frequency distribution of pixels of a given color in each line or column in the partial region; each run length determination module receives an image of each partial region of the recognition target region and determines whether or not a line or column of pixels of the given color having a certain length is present in the partial region; a control module feeds pixel information of the partial regions, read by scanning the binarized image stored in the image memory, into the histogram computation modules and the run length determination modules; a determination module determines a symbol included in the binarized image based on computation results of the histogram computation modules and determination results of the run length determination modules.

The subject matter of this specification can be implemented in, among other things, a method that includes identifying edges of a section of a document in a source image that includes at least one row of text. The method includes identifying characters in the document. The method includes identifying word portions. The method includes generating polynomials that approximate points of the characters within the word portions. The method includes generating a second polynomial that approximates the points of the characters of word portions. The method includes identifying a stretching coefficient of the row of text based on a length of the section between the edges relative to a length of the second polynomial. The method includes mapping portions of the source image along the row of text to new positions in a corrected image based on the second polynomial and the stretching coefficient.

A method for locating machine-readable zones in document images based on feature points is disclosed. In an embodiment, feature points are found in the image, and linear objects are located in the image (e.g., by applying a Fast Hough Transform to the image). The feature points are filtered based on their correspondence to the linear objects. The filtered feature points are grouped into clusters, and rectangular zones are defined around each cluster. A final rectangular zone is selected from the defined rectangular zones. This method of locating machine-readable zones is designed to meet the requirements for real-time operation on mobile devices.

A method for locating machine-readable zones in document images based on feature points is disclosed. In an embodiment, feature points are found in the image, and linear objects are located in the image (e.g., by applying a Fast Hough Transform to the image). The feature points are filtered based on their correspondence to the linear objects. The filtered feature points are grouped into clusters, and rectangular zones are defined around each cluster. A final rectangular zone is selected from the defined rectangular zones. This method of locating machine-readable zones is designed to meet the requirements for real-time operation on mobile devices.

The present disclosure relates to a system and computer-implemented method for character recognition in a payment card. The method includes receiving an image of a payment card and one or more details associated with the payment card. Further, a derivative of the image is determined based on the one or more details and a horizontal sum of pixel values is determined for a plurality of rows in the image. Furthermore, one or more Regions of Interest (ROIs) are identified in the image by comparing the horizontal sum of pixel values with a predefined first threshold. Subsequently, one or more characters in the one or more ROIs are extracted using one or more peak values in a histogram of the one or more ROIs. Finally, each of the one or more characters extracted from the one or more ROIs is recognized using a trained Artificial Intelligence technique.

The present disclosure relates to a system and computer-implemented method for character recognition in a payment card. The method includes receiving an image of a payment card and one or more details associated with the payment card. Further, a derivative of the image is determined based on the one or more details and a horizontal sum of pixel values is determined for a plurality of rows in the image. Furthermore, one or more Regions of Interest (ROIs) are identified in the image by comparing the horizontal sum of pixel values with a predefined first threshold. Subsequently, one or more characters in the one or more ROIs are extracted using one or more peak values in a histogram of the one or more ROIs. Finally, each of the one or more characters extracted from the one or more ROIs is recognized using a trained Artificial Intelligence technique.

A method for image processing that includes: obtaining a mask of a connected component (CC) from an image; generating a stroke width transform (SWT) image based on the mask; calculating multiple stroke width parameters for the mask based on the SWT image; identifying a hole in the CC of the mask; calculating a stroke width estimate for the hole based on the stroke width values of pixels in the SWT image surrounding the hole; generating a comparison of the stroke width estimate for the hole with a limit based on the multiple stroke width parameters for the mask; and generating a revised mask by filling the hole in response to the comparison.

A handwriting geometry recognition and calibration system by using neural network and mathematical feature includes: a pre-processor for pre-processing coordinate points of geometric figures from user's handwriting so as to get a plurality of sample points which expresses the geometric figures to be recognized; a neural network connected to the pre-processor for receiving the sample points of the geometric figure and recognizing the geometric figure so as to acquire a coarse class of the geometric figure; and an mathematical logic unit connected to the neural network for receiving recognition results from the neural network, including coarse classifications which are used in a secondary classification by using conventional mathematical recognition logics so as to determine an exact geometry shape of the geometric figure; then the geometric figure being calibrated so as to get a geometry with a regular shape.

A handwriting geometry recognition and calibration system by using neural network and mathematical feature includes: a pre-processor for pre-processing coordinate points of geometric figures from user's handwriting so as to get a plurality of sample points which expresses the geometric figures to be recognized; a neural network connected to the pre-processor for receiving the sample points of the geometric figure and recognizing the geometric figure so as to acquire a coarse class of the geometric figure; and an mathematical logic unit connected to the neural network for receiving recognition results from the neural network, including coarse classifications which are used in a secondary classification by using conventional mathematical recognition logics so as to determine an exact geometry shape of the geometric figure; then the geometric figure being calibrated so as to get a geometry with a regular shape.