A generation method implemented by a computer, the generation method including: acquiring, by a processor circuit of the computer, read information generated from a reading result that is a document image obtained by imaging a paper document; and generating, by the processor circuit, an electronic document with a signature image that includes the electronic document and the signature image by adding the signature image obtained by imaging a signature written or stamped on the paper document to an electronic document that corresponds to the acquired read information.

A generation method implemented by a computer, the generation method including: acquiring, by a processor circuit of the computer, read information generated from a reading result that is a document image obtained by imaging a paper document; and generating, by the processor circuit, an electronic document with a signature image that includes the electronic document and the signature image by adding the signature image obtained by imaging a signature written or stamped on the paper document to an electronic document that corresponds to the acquired read information.

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.

Provided are a text recognition method, an electronic device, and a non-transitory computer-readable storage medium, which are applicable in an OCR scenario. In the particular solution, a text image to be recognized is acquired. Feature extraction is performed on the text image, to obtain an image feature corresponding to the text image, where a height-wise feature and a width-wise feature of the image feature each have a dimension greater than 1. According to the image feature, sampling features corresponding to multiple sampling points in the text image are determined. According to the sampling features corresponding to the multiple sampling points, a character recognition result corresponding to the text image is determined.

Provided are a text recognition method, an electronic device, and a non-transitory computer-readable storage medium, which are applicable in an OCR scenario. In the particular solution, a text image to be recognized is acquired. Feature extraction is performed on the text image, to obtain an image feature corresponding to the text image, where a height-wise feature and a width-wise feature of the image feature each have a dimension greater than 1. According to the image feature, sampling features corresponding to multiple sampling points in the text image are determined. According to the sampling features corresponding to the multiple sampling points, a character recognition result corresponding to the text image is determined.

Method, computer readable medium, and apparatus of recognizing character zone in a digital document. In an embodiment, the method includes classifying a segment of the digital document as including text, calculating at least one parameter value associated with the classified segment of the digital document, determining, based on the calculated at least one parameter value, a zonal parameter value, classifying the segment of the digital document as a handwritten text zone or as a printed text zone based on the determined zonal parameter value and a threshold value, the threshold value being based on a selection of an intersection of a handwritten text distribution profile and a printed text distribution profile, each of the handwritten text distribution profile and the printed text distribution profile being associated with a zonal parameter corresponding to the determined zonal parameter value, and generating, based on the classifying, a modified version of the digital document.

Methods and systems for artificial intelligence (AI)-assisted document annotation and training of machine learning-based models for document data extraction are described. The methods and systems described herein take advantage of a continuous machine learning approach to create document processing pipelines that provide accurate and efficient data extraction from documents that include structured text, semi-structured text, unstructured text, or any combination thereof.

A display apparatus includes circuitry that receives a plurality of stroke data input to a touch panel by an input device and displays the plurality of stroke data. The plurality of stroke data includes first stroke data and second stroke data input after the first stroke data. The circuitry sets a determination area differently depending on whether an elapsed time from separation of the input device from the touch panel after input of the first stroke data exceeds a threshold. The determination area is for determining whether to include the second stroke data in a recognition group including the first stroke data. The circuitry performs character recognition on the recognition group and displays, on a screen, a result of the character recognition.

A method and system are used for managing and classifying electronic document images. Each of the electronic document images is divided into an array of image segments. The method extracts image features from each of the image segments to obtain numerical coefficients for each of the image segments. The numerical coefficients are compared with each other to generate sub-codes. A classification code is determined as a combination of the sub-codes. The classification codes of a plurality of electronic document images can be stored in a database for further analysis. Based on the classification codes, similarity rates between at two document images can be determined.

A character segmentation method and apparatus, and a computer-readable storage medium. The character segmentation method comprises: converting a character area image into a grayscale image (step 101); converting the grayscale image into an edge binary image by using an edge detection algorithm (step 102); acquiring character box segmentation blocks from the edge binary image by using a projection method (step 103); and determining a target character area from the character box segmentation blocks by using a contour detection algorithm, and performing character segmentation on the character area image according to the target character area (step 104). Another character segmentation method comprises: converting a character area image into a grayscale image (step 701); performing clustering analysis on the grayscale image by using a fuzzy C-means clustering algorithm, and executing binarization processing on the grayscale image according to a clustering analysis result (step 702); acquiring character positioning blocks from a binary image by using a projection method (step 703); and performing character segmentation on the character area image according to position information of the character positioning blocks (step 704). By using the methods and apparatuses, character segmentation can be performed on a relatively low quality image.