Character recognition is described. In one embodiment, it may use matched sequences rather than character shape to determine a computer-legible result.

According to one embodiment, a data processor includes an image acquisition module, a degradation evaluation module, a first output module and a display module. The first output module is configured to output a first trigger for performing a process for detecting the image area when the possibility is high as a result of evaluation by the degradation evaluation module, the first output module is configured to output a command for displaying the image as it is on a display when the possibility is low as a result of the evaluation.

An image processing apparatus, system, method, and control program stored in a non-transitory recording medium are provided each of which obtains image data of a document; determines an arrangement pattern of each of a plurality of character strings in the image data, based on positional relationship of the plurality of character strings; and generates a text data file including the plurality of character strings each being arranged according to the arrangement pattern that is determined.

An information processing apparatus includes a processor configured to obtain, for each character of plural characters recognized from an image, (a) position of the character in the image, (b) size of the character, and (c) confidence level of a character recognition result of the character; and determine whether to regard the character as a noise based on a distance between the character and its nearest character, the size of the character, and the confidence level of the character recognition result of the character.

An information processing apparatus includes a processor configured to acquire (i) an image including characters and (ii) a character-recognition result obtained by applying character recognition on the image, and display, to a viewer of the character-recognition result, each character in the image and a recognized character corresponding to the character in a uniform size and at positions adjusted to indicate correspondence between the character and the recognized character.

A method to improve the efficacy of optical character recognition (OCR) includes scanning an electronically stored representation of a whole or partial document, identifying an image having text in the electronically stored representation of a whole or partial document, identifying the text within the image, and generating a plurality of bounding boxes around the identified text using blob detection. The method also includes grouping together certain text bounding boxes of the plurality of text bounding boxes that are vertically aligned with each other to generate a plurality of aligned text bounding boxes and performing OCR on the aligned text bounding boxes to generate a plurality of OCR groups of text. In addition, the method includes generating a resultant representation of a whole or partial document electronically using the plurality of OCR groups of text and saving the resultant representation of a whole or partial document electronically.

A system for determining compression rates for images, the system comprising a processing resource configured to: obtain a given image at least partially comprising a given text; compress the given image at a given compression ratio, giving rise to a compressed image; perform Optical Character Recognition (OCR) on the compressed image, giving rise to OCR text; compare the OCR text to the given text, giving rise to comparison results; upon the comparison results meeting a rule, increase the given compression rate; and upon the compression results not meeting a rule, return to a previous compression rate, if any.

A method and system for segmenting touching text lines in an image of a uchen-script Tibetan historical document are provided. The method includes: first obtaining a binary image of a uchen-script Tibetan historical document after layout analysis; detecting local baselines in the binary image, to generate a local baseline information set; detecting and segmenting a touching region in the binary image according to the local baseline information set, to generate a touching-region-segmented image; allocating connected components in the touching-region-segmented image to corresponding lines, to generate a text line allocation result; and splitting text lines in the touching-region-segmented image according to the text line allocation result, to generate a line-segmented image. In the present disclosure, touching text lines in a Tibetan historical document can be effectively segmented, and text line segmentation efficiency of the Tibetan historical document is improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural network-based optical character recognition. An embodiment of the system may generate a set of bounding boxes based on reshaped image portions that correspond to image data of a source image. The system may merge any intersecting bounding boxes into a merged bounding box to generate a set of merged bounding boxes indicative of image data portions that likely portray one or more words. Each merged bounding box may be fed by the system into a neural network to identify one or more words of the source image represented in the respective merged bounding box. The one or more identified words may be displayed by the system according to a standardized font and a confidence score.

A system is provided for a distributed server network with embedded image decoder as a chain code program runtime event. In particular, the system may comprise a distributed computing network comprising one or more decentralized nodes, each of which may store a separate copy of a distributed data register. The system may further comprise one or more specialized nodes which receive, assess, and analyze user input data, where the one or more specialized nodes may include a client identity node comprising an embedded image decoder which may be configured to analyze image portions of the user input data. Once the image data has been analyzed, the client identity node may convert the image data into a text format for storage within the distributed register.