A method and a system for digitization of a document are disclosed. The document is scanned to generate an electronic document. One or more characters in a first set of portions of the electronic document are identified, based on a character recognition technique. Each portion in the first set of portions is classified in one or more groups based on at least a status of identification of the corresponding one or more characters. Further, one or more tasks are created for each of the one or more groups. The one or more tasks are transmitted to one or more crowdworkers, based at least on the respective type of the one or more tasks. Further, a response for each of the one or more tasks is received. Based on the received response, a digitized document is generated.

The method, system, and non-transitory computer-readable medium embodiments described herein provide for identifying invalid identification documents. In various embodiments, an application executing on a user device prompts the user device to transmit an image of the identification document. The application receives an image including the identification document in response to the identification document being within a field of view of a camera of the user device. The identification document includes a plurality of visual elements, and one or more visual elements of the plurality of visual elements are one or more invalidating marks. The application detects a predetermined pattern on the identification document in the image, the predetermined pattern formed from the one or more invalidating marks. The application determines that the identification document is invalid based on the detected predetermined pattern.

An apparatus including a processor caused to receive document images, each including representations of characters. The processor is caused to parse each document image to extract, based on structure type, subsets of characters, to generate a text encoding for that document image. For each document, the processor is caused to extract visual features to generate a visual encoding for that document image, each visual feature associated with a subset of characters. The processor is caused to generate parsed documents, each parsed document uniquely associated with a document image and based on the text and visual encoding for that document image. For each parsed document, the processor is caused to identify sections uniquely associated with section type. The processor is caused to train machine learning models, each machine learning model associated with one section type and trained using a portion of each parsed document associated with that section type.

Various methods, apparatuses/systems, and media for data processing are disclosed. A processor receives a digital document; applies an optical character recognition (OCR) algorithm on said received digital document by utilizing an OCR tool; identifies defective data extracted by the OCR tool resulted from relatively inferior image quality of the received digital document; implements an auto rectification algorithm on the identified defective data; automatically generates, in response to implementing the auto rectification algorithm, corresponding auto-rectified data for each identified defective data; records the defective data and corresponding auto-rectified data at a field level; receives user input data on said recorded auto-rectified data; determines whether the auto-rectified data is correct or not; and populates, based on determining that the auto-rectified data is correct, a machine learning model with said received user input data to be utilized for subsequently received digital document.

An apparatus including a processor caused to receive document images, each including representations of characters. The processor is caused to parse each document image to extract, based on structure type, subsets of characters, to generate a text encoding for that document image. For each document, the processor is caused to extract visual features to generate a visual encoding for that document image, each visual feature associated with a subset of characters. The processor is caused to generate parsed documents, each parsed document uniquely associated with a document image and based on the text and visual encoding for that document image. For each parsed document, the processor is caused to identify sections uniquely associated with section type. The processor is caused to train machine learning models, each machine learning model associated with one section type and trained using a portion of each parsed document associated with that section type.

Systems, methods, devices and computer readable media for determining a geographical location of a license plate are described herein. A first image of a license plate is acquired by a first image acquisition device of a camera unit and a second image of the license plate is acquired by a second image acquisition device of the camera unit. A three-dimensional position of the license plate relative to the camera unit is determined based on stereoscopic image processing of the first image and the second image. A geographical location of the camera unit is obtained. A geographical location of the license plate is determined from the three-dimensional position of the license plate relative to the camera unit and the geographical location of the camera unit. Other systems, methods, devices and computer readable media for detecting a license plate and identifying a license plate are described herein.

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.

The current modular implant is particularly useful in spinal surgical procedures. The modular implant can be provided with two anchors and a central section.

The present disclosure involves systems, software, and computer-implemented methods for allowing independent adjustment for different areas of a payment instrument image. An example method includes updating an image property of an area of a clearing payment instrument image associated with a tangible payment instrument including a payee, a payor, an amount, and an authorization, the tangible payment instrument to be submitted for electronic transaction clearing, wherein the clearing payment instrument image is associated with a first value of the image property, the image property of the area is updated to a second value different than the first value of the image property, and the area of the clearing payment instrument image includes less than the entire clearing payment image; and storing the updated clearing payment instrument image in response to updating the image property.

The technology of the present disclosure includes computer-implemented methods, computer program products, and systems to filter images before transmitting to a system for optical character recognition (OCR). A user computing device obtains a first image of the card from the digital scan of a physical card and analyzes features of the first image, the analysis being sufficient to determine if the first image is likely to be usable by an OCR algorithm. If the user computing device determines that the first image is likely to be usable, then the first image is transmitted to an OCR system associated with the OCR algorithm. Upon a determination that the first image is unlikely to be usable, a second image of the card from the digital scan of the physical card is analyzed. The optical character recognition system performs an optical character recognition algorithm on the filtered card.