Abstract: Methods, apparatus, and articles manufacture to decode documents based on images using artificial intelligence are disclosed. An example apparatus includes a model executor to input an image into a first artificial intelligence (AI)-based model to generate detected columns of text in the image; and input the image into a second AI-based model to classify the detected columns into categories; a cell identifier to identify rows or cells in the detected columns; and a report generator to: link information corresponding to the rows or cells in the detected columns with corresponding categories; and generating a report based on the linked information.

Abstract: A system and method is disclosed for improving fraud detection in the context of a user submitting, via a client device, a photo of a photo ID and a selfie taken during a step of the verification process. A machine learning model may be trained to generate biometric data signals from the selfie. As examples, the biometric data signals generated from the selfie can include an estimated age, gender, hair color, and eye color. The biometric data generated from the selfie may be compared with biometric data listed on the photo ID as an aid to identifying potential identity fraud. In some implementations, a facial filter corresponding to a map of a set of facial measurements of the photo in the photo ID may be compared with the facial measurements of the selfie.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to decode receipts based on neural graph architecture. An apparatus includes interface circuitry to obtain an image of a document; machine readable instructions; and programmable circuitry to execute the machine readable instructions to at least generate nodes for a feature graph based on features extracted from text boxes, the nodes including polar coordinates indicative of angular positions of the text boxes; pass the feature graph through a graph neural network to generate an adjacency matrix; and identify text lines in the image of the document by clustering the nodes by line based on the adjacency matrix.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to decode receipts based on neural graph architecture. An example apparatus for decoding receipts includes, vertex feature representation circuitry to extract features from optical-character-recognition (OCR) words, polar coordinate circuitry to: calculate polar coordinates of the OCR words based on respective ones of the extracted features, graph neural network circuitry to generate an adjacency matrix based on the extracted features, post-processing circuitry to traverse the adjacency matrix to generate cliques of OCR processed words, and output circuitry to generate lines of text based on the cliques of OCR processed words.

Abstract: Methods, systems, articles of manufacture and apparatus are disclosed to label text on images. An example apparatus includes colorizer circuitry to apply color to text boxes corresponding to optical character recognition (OCR) data associated with an image, OCR manager circuitry to render an OCR text prompt associated with the OCR data, the OCR text prompt to be rendered proximate to respective ones of the text boxes, the OCR text prompt to display a text portion of the OCR data, and edit circuitry to (a) render an interface in response to selection of the OCR text prompt, the interface populated with the text portion of the OCR data, and (b) in response to an overwrite input to the interface, update the text portion of the OCR data in a memory corresponding to the image.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to decode receipts based on neural graph architecture. An example apparatus for decoding receipts includes, vertex feature representation circuitry to extract features from optical-character-recognition (OCR) words, polar coordinate circuitry to: calculate polar coordinates of the OCR words based on respective ones of the extracted features, graph neural network circuitry to generate an adjacency matrix based on the extracted features, post-processing circuitry to traverse the adjacency matrix to generate cliques of OCR processed words, and output circuitry to generate lines of text based on the cliques of OCR processed words.

Abstract: Methods, apparatus, and articles manufacture to decode documents based on images using artificial intelligence are disclosed. An example apparatus includes a model executor to input an image into a first artificial intelligence (AI)-based model to generate detected columns of text in the image; and input the image into a second AI-based model to classify the detected columns into categories; a cell identifier to identify rows or cells in the detected columns; and a report generator to: link information corresponding to the rows or cells in the detected columns with corresponding categories; and generating a report based on the linked information.