A method and apparatus for optical character recognition of dot text in an image are described. A plurality of dots that satisfy dot selection criteria are extracted from an image. A transformation is performed on the plurality of dots based on a first candidate distance between adjacent dots along a first orientation and on a second candidate distance between adjacent dots along a second orientation to obtain a transformed image including stroked characters. Model based character matching is performed on the transformed image to output a candidate string of characters.

The present disclosure relates to the extraction of text from an image including a depiction of a document. According to one embodiment, a mobile device receives an image depicting a document. The mobile device identifies a plurality of text areas in the document and identifies a midpoint of each of the plurality of text areas in the document. The mobile device detects one or more lines of text in the document including a plurality of text areas, where the plurality of text areas included in a line of text are associated with a midpoint having a coordinate within a threshold number of pixels on one axis in a two-dimensional space. Based on an orientation of the detected one or more lines of text, the mobile device determines a probable orientation of the document and extracts text from the image based on the determined probable orientation of the document.

The present disclosure relates to the extraction of text from an image including a depiction of a document. According to one embodiment, a mobile device receives an image depicting a document. The mobile device identifies a plurality of text areas in the document and identifies a midpoint of each of the plurality of text areas in the document. The mobile device detects one or more lines of text in the document including a plurality of text areas, where the plurality of text areas included in a line of text are associated with a midpoint having a coordinate within a threshold number of pixels on one axis in a two-dimensional space. Based on an orientation of the detected one or more lines of text, the mobile device determines a probable orientation of the document and extracts text from the image based on the determined probable orientation of the document.

In some implementations, a device may receive an image of a document, the image depicting a reference feature associated with the document, the reference feature including at least one of: a face of a person, a machine-readable code, or a text field. The device may identify a rotational angle of the reference feature as depicted in the image based on comparing the reference feature as depicted in the image to one or more orientation parameters of the reference feature associated with a display orientation associated with the document. The device may rotate the image of the document by an angle to obtain an orientated image of the document, the angle being based on the rotational angle of the reference feature as depicted in the image. The device may provide the orientated image of the document for display.

A system is discussed herein that is configured for extracting data from documents. In particular, the system may be utilized for automating and computerized checking of transit and shipping related documents. For example, the documents may include various data, such delivery dates, prices, inventory identification, personnel identification, container identification, customs documents, transport documents, a combination thereof, and the like.

A detection-based segmentation-free method and system for license plate recognition. An image of a vehicle is initially captured utilizing an image-capturing unit. A license plate region is located in the image of the vehicle. A set of characters can then be detected in the license plate region and a geometry correction performed based on a location of the set of characters detected in the license plate region. An operation for sweeping an OCR across the license plate region can be performed to infer characters with respect to the set of characters and locations of the characters utilizing a hidden Markov model and leveraging anchored digit/character locations.

A method includes: displaying a digital image on a first portion of a display of a mobile device; receiving user feedback via the display of the mobile device; analyzing the user feedback to determine a meaning of the user feedback; based on the determined meaning of the user feedback, analyzing a portion of the digital image corresponding to either the point of interest or the region of interest to detect one or more connected components depicted within the portion of the digital image; classifying each detected connected component depicted within the portion of the digital image; estimating an identity of each detected connected component based on the classification of the detected connected component; and one or more of: displaying the identity of each detected connected component on a second portion of the display of the mobile device; and providing the identity of each detected connected component to a workflow.

Systems and methods provide a deployable cloud-agnostic redaction container for performing optical character recognition and redacting information from a document using a cloud-based, guided redaction framework. An example method for document redaction includes receiving a plurality of documents and extracting pages from the plurality of documents. The method then determines, based on a load balancing criterion, a processing order for the pages extracted from the plurality of documents, and performs, based on the processing order, an optical character recognition process and a redaction process on the pages to generate redacted pages. The redacted pages are provided for transmission or storage to a cloud data management platform.

An image reading apparatus includes a character recognition processing unit, an incorrect recognition index calculator, a certainty calculator, a direction determining unit, and an image processing unit. The incorrect recognition index calculator calculates incorrect recognition indexes. The incorrect recognition index is set based on a count of incorrect recognition characters. The count of incorrect recognition characters is a count of candidates for characters possibly incorrectly recognized when the documents are read. The incorrect recognition index is set such that recognition certainty indicative of accuracy of the recognition becomes smaller as the count of incorrect recognition characters increases. The certainty calculator adjusts the recognition certainty using the incorrect recognition index. The direction determining unit that determines a direction of the documents based on the adjusted recognition certainty. The image processing unit corrects the image data based on the determined document direction to align image directions of the plurality of documents.

A method of image-to-document conversion based on optical character recognition (OCR) includes obtaining an image to be converted into a target document, and performing layout segmentation on the image according to image content of the image, to obtain n image layouts, each of the n image layouts corresponding to a content type, and n being a positive integer. The method also includes, for each of the n image layouts, processing image content in the respective image layout according to the content type corresponding to the respective image layout, to obtain converted content corresponding to the respective image layout. The method further includes adding the converted content corresponding to the n image layouts to an electronic document, to obtain the target document.