The present disclosure is generally directed to systems and methods for executing optical character recognition faster than at least some traditional OCR systems, without sacrificing recognition accuracy. Towards this end, various exemplary embodiments involve the use of a bounding box and a grid-based template to identify certain unique aspects of each of various characters and/or numerals. For example, in one embodiment, the grid-based template can be used to recognize a numeral and/or a character based on a difference in centerline height between the numeral and the character when a monospaced font is used. In another exemplary embodiment, the grid-based template can be used to recognize an individual digit among a plurality of digits based on certain parts of the individual digit being uniquely located in specific portions of the grid-based template.

Detection of typed and/or pasted text, caret tracking, and active element detection for a computing system are disclosed. The location on the screen associated with a computing system where the user has been typing or pasting text, potentially including hot keys or other keys that do not cause visible characters to appear, can be identified and the physical position on the screen where typing or pasting occurred can be provided based on the current resolution of where one or more characters appeared, where the cursor was blinking, or both. This can be done by identifying locations on the screen where changes occurred and performing text recognition and/or caret detection on these locations. The physical position of the typing or pasting activity allows determination of an active or focused element in an application displayed on the screen.

The present disclosure discloses methods and systems for automatically detecting Infrared (IR) security mark based on unknown halftone frequency information. The method includes receiving a document from a user including an IR security mark. The document is scanned. Then, one or more halftone frequencies associated with the IR security mark portion are estimated. Based on the estimation, the IR security mark portion is classified into a background region and the IR marked region including the IR security mark. The IR security mark is extracted and pixels falling in the IR marked region are reconstructed to identify content in the IR security mark. Finally, the identified content is compared with one or more pre-stored IR security marks to ascertain the presence of the IR security mark in the document for further assessment. This way, the method automatically detects the IR security mark in the document.

Embodiments for automatically converting printed documents into electronic format using artificial intelligence techniques disclosed herein include: (i) receiving a plurality of images of documents; (ii) for each received image, using an image classification algorithm to classify the image as one of (a) an image of a first type of document, or (b) an image of a second type of document; (iii) for each image classified as an image of the first type of document, using an object localization algorithm to identity an area of interest in the image; (iv) for an identified area of interest, using an optical character recognition algorithm to extract text from the identified area of interest; and (v) populating a record associated with the document with the extracted text.

An image processing apparatus comprises an execution unit configured to selectively execute, for each pixel in each of a first region, a second region, and an intermediate region, one of a plurality of processes including first processing of generating an output value indicating that a first printing material is applied and a second printing material of a color different from a color of the first printing material is not applied, and second processing of generating an output value indicating that the second printing material is applied.

A focus detection method includes: acquiring an image of a test object through a to-be-tested image acquisition device, the test object including a character, and a clarity of the character corresponding to a minimum clarity with which a content of the character is still able to be recognized using a character recognition technology; performing character recognition on the image to obtain a recognition result; and determining a focus detection result for the to-be-tested image acquisition device based on the recognition result.

An information processing apparatus includes a processor configured to: acquire information indicating a size of an external shape of each of characters in data of a first image and data of a second image that are used for comparison; and determine a presence or absence of a fault in the data of the second image with respect to each of the characters with reference to a degree of a difference that is between the data of the second image and the data of the second image and is detected in accordance with a detection condition varying in response to the size of the external shape of each of the characters.

This disclosure relates to a method for recognizing multimedia content. The method includes: obtaining target text information and content information in a video; performing text recognition processing on the content information to obtain associated text information; when the original text information or the associated text information meets a first malicious promotion condition, obtaining a target text classification result by a text classification model; and determining a video recognition result corresponding to the video according to the target text classification result.

Methods and systems for extracting and processing data using optical character recognition in real-time environments. For example, the methods and systems provide novel techniques during extracting data using OCR and for a mechanism to process that data. These methods and systems are particularly relevant in real-time environments as the methods and system limit the need for manual review.

A method of image analysis is provided for recognition of a pattern in an image. The method includes receiving a plurality of images acquired by a camera, where the plurality of images include a plurality of optical patterns in an arrangement. The method also includes matching the arrangement to a pattern template, wherein the pattern template is a predefined arrangement of optical patterns. The method also includes identifying an optical pattern of the plurality of optical patterns as a selected optical pattern based on a position of the selected optical pattern in the arrangement. The method also includes decoding the selected optical pattern to generate an object identifier and storing the object identifier in a memory device.