An electronic device and method are disclosed. The electronic device includes a touch-sensitive display, a memory and a processor. The processor implements the method, including: detect a written input including a plurality of strokes through the display, group the plurality of strokes into a first group and a second group based on respective coordinates of each of the plurality of strokes, group first strokes included in the first group into a plurality of blocks, based on a distance between respective coordinates of each of the first strokes, determine a slope for each of the plurality of blocks, rotate an area corresponding to the first group based on the determined slope, execute handwriting recognition on the first strokes based on the rotated area, and displaying a result of the handwriting recognition on the display.

Described are approaches for assigning tasks between machine resources (e.g., AI task performers, AI task validators), human resources (e.g., task performers, task validators), and/or other smart systems to facilitate collaborative text detection, text recognition, and text retrieval in order to optimize system performance along a variety of different selection criteria specifying various performant dimensions, including, but not limited to improving system efficiency, reducing task performer and/or task validator idle time, improving triage outcomes, reducing data processing loads, maintaining client confidentiality, etc., that may be associated with one or more customers.

An image processing system performs tilt correction with respect to a document image having handwritten characters and typed letters mixed with each other. The image processing system separates the document image into an image with handwritten characters determined as handwritten characters and an image without handwritten characters not determined as handwritten characters, estimates a tilt angle of the image without handwritten characters, and corrects the document image on the basis of the tilt angle.

Described are approaches for assigning tasks between machine resources (e.g., AI task performers, AI task validators), human resources (e.g., task performers, task validators), and/or other smart systems to facilitate collaborative text detection, text recognition, and text retrieval in order to optimize system performance along a variety of different selection criteria specifying various performant dimensions, including, but not limited to improving system efficiency, reducing task performer and/or task validator idle time, improving triage outcomes, reducing data processing loads, maintaining client confidentiality, etc., that may be associated with one or more customers.

A system for file image processing and extraction of content from images is provided. The system comprises a computer and an application. When executed on the computer, the application receives a source document containing areas of interest and normalizes the document to align with a stored template image. The application also applies metadata associated with the template image to the areas of interest to identify data fields in the normalized document and extracts data from the identified data fields. The application also processes the extracted data using at least character recognition systems and produces a static structure using at least the identified data fields, the fields containing the processed data. The areas of interest comprise portions of the source document containing text needed to create and populate fields suggested by the stored template image. Normalizing the source document comprises at least one of flipping, rotating, expanding, and shrinking the document.

A monocular visual simultaneous localization and mapping (SLAM) data processing method. The SLAM data processing method comprises: obtaining rotation angular velocities and accelerations of a camera cyclically; obtaining a plurality of feature point pairs in two frames of images acquired by the camera, and obtaining pixel coordinate values of feature points in the feature point pairs, where each of the feature point pairs includes two feature points that correspond to a same feature of a same object and that are respectively in the two frames of images; obtaining to-be-selected rotation matrices and to-be-selected displacement matrices according to the pixel coordinate values; obtaining a reference rotation matrix of the camera according to the rotation angular velocities, and obtaining a reference displacement matrix of the camera according to the accelerations; and filtering the to-be-selected rotation matrices and the to-be-selected displacement matrices according to the reference rotation matrix and the reference displacement matrix.

Methods, apparatus, systems, and articles of manufacture are disclosed to decode receipts based on neural graph architecture. An example apparatus for decoding receipts includes, vertex feature representation circuitry to extract features from optical-character-recognition (OCR) words, polar coordinate circuitry to: calculate polar coordinates of the OCR words based on respective ones of the extracted features, graph neural network circuitry to generate an adjacency matrix based on the extracted features, post-processing circuitry to traverse the adjacency matrix to generate cliques of OCR processed words, and output circuitry to generate lines of text based on the cliques of OCR processed words.

A display apparatus includes circuitry to receive an operation of changing a direction of display of a character string displayed in a first direction on a display, and control the display to display a converted character string in a second direction corresponding to the operation of changing. The converted character string is converted from the character string into a target language associated with the second direction.

Provided is an image reading system that divides, with respect to image data obtained by performing a double sided reading in a state where a booklet is opened, cover sheet image data into two parts corresponding to a pair of cover sheets to arrange the two parts at a front and an end, respectively, and arranges main text image data between the front and the end, and then generates an image file from each of the arranged image data.

An image forming apparatus includes circuitry. The circuitry generates a binary image having area gradation or a scaled image having area gradation from an image read by a scanner. The circuitry outputs classification of the binary image or the scaled image according to a neural network model learned in advance.