A method of recognizing a text, which relates to a field of an artificial intelligence technology, in particular to a field of computer vision and deep learning technology, and may be applied to optical character recognition or other applications. The method includes: acquiring a plurality of image sequences by continuously scanning a document; performing an image stitching, so as to obtain a plurality of successive frames of stitched images corresponding to the plurality of image sequences respectively, an overlapping region exists between each two successive frames of stitched images; performing a text recognition based on the plurality of successive frames of stitched images, so as to obtain a plurality of corresponding recognition results; and performing a de-duplication on the plurality of recognition results based on the overlapping region between each two successive frames of stitched images, so as to obtain a text recognition result for the document.

The system captures portions of a label on a package in a set of images, reconstructs the label based on the set of images, identifies text in the label, determines associations of identified text and types of information, and stores the set of images, the reconstructed label, the identified text in the label, and the determined associations as, for example, a batch in a review queue. During a review process, the batch is reviewed and a structured prescription record is determined for the batch which is further used by the system and user of the system associated with the batch to provide various features to the user.

A method for optical character recognition of text and information on a curved surface, comprising: activating an image capture device; scanning of the surface using the image capture device in order to acquire a plurality of scans of sections of the surface; performing OCR on the plurality of scans; separating the OCRed content into layers for each of the plurality of scans; merging the separated layers into single layers; and merging the single layers into an image.

A method for optical character recognition of text and information on a curved surface, comprising: activating an image capture device; scanning of the surface using the image capture device in order to acquire a plurality of scans of sections of the surface; performing OCR on the plurality of scans; separating the OCRed content into layers for each of the plurality of scans; merging the separated layers into single layers; and merging the single layers into an image.

A method, a computer program product, and a computer system compare images for content consistency. The method includes receiving a first image including a first document and a second image including a second document. The method includes performing a visual classification analysis on the first image and the second image. The visual classification analysis generates an overlap of the first image with the second image. The method includes determining whether a region of the overlap is indicative of a content inconsistency. As a result of the region of the overlap being indicative of a content inconsistency, the method includes performing a character recognition analysis on a first area of the first image and a second area of the second image corresponding to the region of the overlap to verify the content inconsistency.

Methods for managing notes, either digital notes or images of physical notes. The methods use optical character recognition to convert handwritten content into characters and icons. The methods also determine sizes of non-square physical notes when imaged by a camera from various angles. For curled notes or damaged notes such as a note missing a corner, the methods detect and process edges of a physical note in an image and, using the detected edges, convert the image of the physical note into a corresponding digital note without curl or damage.

A marking region image is obtained by cutting out the part corresponding to a marking region from an article image obtained by imaging an article to be inspected. Then, whether or not the marking is properly provided is determined by performing a character recognition of a marking part for a marking region image. Further, an image of an article having no marking and no defect is stored as a reference image, whereas a marking periphery image obtained by removing the image of the marking part from the marking region image is compared to the reference image. By that comparison, whether or not any defect is included in the marking peripheral part of the marking region except the marking part is determined.

Method for determining image reconstruction parameters in an optical biometric imaging device comprising a plurality of microlenses forming a microlens array and an image sensor arranged to receive light having passed through the microlenses, the method comprising: by the image sensor, capturing a plurality of sub-images together representing an image of a biometric object in contact with a sensing surface of the biometric imaging device, each sub-image corresponding to a respective microlens; and determining a demagnification factor based on at least a subset of the plurality of sub-images.

An image processing apparatus that generates an image for character recognition from a read image includes at least one memory that stores instructions, and at least one processor that executes the instructions to perform extracting of an area of handwritten character information and an area of printed character information from the read image, clipping of a partial image of the area of handwritten character information and a partial image of the area of printed character information out of the read image, and generating of the image for character recognition by combining the partial image of the area of handwritten character information and the partial image of the area of printed character information being associated with each other.

Method for determining image reconstruction parameters in an optical biometric imaging device comprising a plurality of microlenses forming a microlens array and an image sensor arranged to receive light having passed through the microlenses, the method comprising: by the image sensor, capturing a plurality of sub-images together representing an image of a biometric object in contact with a sensing surface of the biometric imaging device, each sub-image corresponding to a respective microlens; and determining a demagnification factor based on at least a subset of the plurality of sub-images.