A system and method for correcting image data is disclosed. The method includes receiving one or more documents from one or more electronic mediums. The method further includes determining a primary character and one or more alternate characters corresponding to the mis-captured character image, extracting one or more confident instances of the primary character and the one or more alternate characters from the one or more documents and generating one or more scores corresponding to the primary character and the one or more alternate characters. Further, the method includes predicting a correct character corresponding to the mis-captured character image by using a trained image prediction-based ML model and automatically replacing the mis-captured character image with the predicted correct character.

To speed up image processing, an obtaining means of an image processing system obtains a captured image of a document that includes a fixed part and an un-fixed part, where the document is captured by an image reader or an image capture device. A first shaping means shapes the captured image based on a feature of the document in a sample image and a feature of the document in the captured image so as to obtain a first shaped image. A detecting means detects a feature part of the fixed part from the first shaped image. A second shaping means shapes the first shaped image such that a position of the feature part detected by the detecting means is aligned with a predetermined position so as to obtain a second shaped image.

Automated document ingestion (ADI) provides a comprehensive system and method to streamline document ingestion automation through developing, deploying, and monitoring machine learning models and tools. The system is designed to integrate alongside existing manual entry pipelines within a company. ADI has multiple components to accomplish each step of this task, namely document enhancements, an augmented data entry user interface, and a machine learning operations (ML Ops) pipeline.

An image processing system performs tilt correction with respect to a document image having handwritten characters and typed letters mixed with each other. The image processing system separates the document image into an image with handwritten characters determined as handwritten characters and an image without handwritten characters not determined as handwritten characters, estimates a tilt angle of the image without handwritten characters, and corrects the document image on the basis of the tilt angle.

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 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.

Computer vision systems and methods for information extraction from inspection tag images are provided. The system receives an image of an inspection tag, detects one or more tags in the image, crops and aligns the image to focus on the detected one or more tags, and processes the cropped and aligned image to automatically extract information from the depicted inspection tag. Each tag identified by the system can be bounded by a tag-box that bounds the detected tag, and a tag quality score can be calculated for each tag-box. One or more visual features can be extracted after cropping of the image, and pixel-level prediction can be performed on the image to predict and/or correct an orientation of the image. Word-level and line-level optical character recognition (OCR) is then performed on the cropped and aligned image of the tag in order to extract a plurality of information from the tag.

A system and method for automating and improving tabular and list-based data extraction from a variety of document types is disclosed. The system and method detect and sort which documents include tables or lists, and performs row and column segmentation. In addition, the system and method apply Conditional Random Fields models to localize each table and semantic data understanding to map and export the extracted data to the desired format and arrangement.

The current application is directed to a method and system for automatically determining the sense orientation of regions of scanned-document images. In one implementation, the sense-orientation method and system to which the current application is directed employs a relatively small set of orientation characters that occur frequently in printed text. In this implementation, for at least one set of orientation characters, each of two or more different orientations of character-containing subregions within a text-containing region of a scanned-document image are compared to each orientation character in the at least one set of orientation characters in order to determine an orientation for each of the character-containing subregions with respect to a reference orientation of the text-containing region. The determined orientations for the character-containing subregions are then used to determine an overall sense orientation for the text-containing region of the scanned-document image.

A computer-implemented method is provided for a mobile device to detect, by a camera of the mobile device, a plurality of checks; determine, by a processing unit of the mobile device, that the image of the plurality of checks is of sufficient quality; instruct, by a display of the mobile device, a user to take a photograph of the plurality of checks; crop, by the processing unit, the photograph of the plurality of checks into a plurality of images, wherein each of the plurality of images contains one of the plurality of checks; and transmit, by a transmitter of the mobile device, the plurality of images to a server via a network. The plurality of images may be transmitted individually (i.e., one at time), or alternatively, collectively and in one payload.