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 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 diagnostic tool for a vehicle includes an imaging device operable to acquire an image of tire characters positioned on an exterior of a vehicle tire. The diagnostic tool includes an optical character recognition (OCR) module configured to identify the tire characters of the tire and an assistance module configured to assist a user to acquire the image of the tire characters with the imaging device. In one example, the diagnostic tool includes a database of vehicle tire information and a database of TPMS tire sensor information. The diagnostic tool is configured to identify a compatible TPMS tire sensor based on the identified tire resulting from the image of the tire characters. In one example, the diagnostic tool is a TPMS tool configured to communicate with a TPMS tire sensor in the vehicle tire.

A diagnostic tool for a vehicle includes an imaging device operable to acquire an image of tire characters positioned on an exterior of a vehicle tire. The diagnostic tool includes an optical character recognition (OCR) module configured to identify the tire characters of the tire and an assistance module configured to assist a user to acquire the image of the tire characters with the imaging device. In one example, the diagnostic tool includes a database of vehicle tire information and a database of TPMS tire sensor information. The diagnostic tool is configured to identify a compatible TPMS tire sensor based on the identified tire resulting from the image of the tire characters. In one example, the diagnostic tool is a TPMS tool configured to communicate with a TPMS tire sensor in the vehicle tire.

Disclosed are methods and systems for using artificial intelligence (AI) for image recognition by using predefined coordinates to extract a portion of a received image, the extracted portion comprising a word to be identified having at least a first letter and a second letter; executing an image recognition protocol to identify the first letter; when the server is unable to identify the second letter, the server executes an AI model having a nodal data structure to identify the second letter based upon the identified first letter, the nodal data structure comprising a set of nodes where each node represents a letter, each node connected to at least one other node, wherein connection of a first node to a second node corresponds to a probability that a letter corresponding to the second node is used in a word subsequent to a letter corresponding to the first node.

A computer-implemented method and apparatus for highlighting text in an image disposed in a markup language document are disclosed. Location data identifying a location, size and orientation of a text element in the image may be obtained, where the text element is oriented in a direction that is non-orthogonal to vertical and horizontal axes of the image. A context for a canvas element in the document may be obtained and rotated to align the context to the orientation of the text element using the location data. The context may also be translated to the location of the text element using the location data, and a text highlighting element that at least partially overlays the text element may be generated.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining analysis profiles, where each analysis profile: corresponds to one of a plurality of detectors, and comprises: a unique set of analysis parameters and/or a unique detection algorithm. The method further includes analyzing image data in accordance with the analysis profiles; selecting an optimum analysis result based on confidence scores associated with different analysis results; and detecting objects within the optimum analysis result. According to additional aspects, the analysis parameters may define different subregions of a digital image to be analyzed; a composite analysis result may be generated based on analysis of the different subregions by different detectors; and the optimum analysis result may be based on the composite analysis result.

A user device detects, in a field of view of the camera, a first side of a document, and determines first information associated with the first side of the document. The user device selects a first image resolution based on the first information and captures, by the camera, a first image of the first side of the document according to the first image resolution. The user device detects, in the field of view of the camera, a second side of the document, and determines second information associated with the second side of the document. The user device selects a second image resolution based on the second information, and captures, by the camera, a second image of the second side of the document according to the second image resolution. The user device performs an action related to the first image and the second image.

In certain embodiments, apparatus has a camera, a screen, and a processor that receives images of a container from the camera and generates displays rendered on the screen. The processor reads a machine-readable code, a lot number, and an expiration date in the container images; uses the code to retrieve corresponding lot number and expiration date stored in a database; determines whether (i) the retrieved lot number matches the read lot number and (ii) the retrieved expiration date matches the read expiration date; and indicates whether or not the information on the container has been successfully verified based on the determination. The processor can determine that the code, lot number, and expiration date are not all visible in a first image generated by the camera and indicate that the container and/or the camera needs to be repositioned to generate another image showing the information missing from the first image.

In certain embodiments, apparatus has a camera, a screen, and a processor that receives images of a container from the camera and generates displays rendered on the screen. The processor reads a machine-readable code, a lot number, and an expiration date in the container images; uses the code to retrieve corresponding lot number and expiration date stored in a database; determines whether (i) the retrieved lot number matches the read lot number and (ii) the retrieved expiration date matches the read expiration date; and indicates whether or not the information on the container has been successfully verified based on the determination. The processor can determine that the code, lot number, and expiration date are not all visible in a first image generated by the camera and indicate that the container and/or the camera needs to be repositioned to generate another image showing the information missing from the first image.