Abstract: Information contained in tables in a digitized document is extracted by retrieving table layout data regarding bounding boxes, each being auto-generated by the system and/or (re)generated by a user to the digitized image of a sample document. A row template is used to identify a first table, by automatically scanning within the document. Upon detecting a possible row in the input image, a Row Possibility Confidence Value (RPCV) is generated that indicates a likelihood that the possible row corresponds to an actual row in the first table. The possible row is regarded as an actual row if the RPCV exceeds a predetermined threshold value. For repeated tables in a document only the first table needs to be identified via bounding boxes. Also, related tables can be linked to permit linked data to be extracted to a structured file. Also, only the primary column in a readable and existent table header is required to extract table values across columns.

Abstract: A system that automatically recognizes checkboxes within a document and extracts the contents thereof. A user views a digitized document and annotates the document by identifying checkboxes contained in the document, by way of visually perceptible bounding boxes. The annotated document is processed by a machine learning engine that employs multiple convolutional operations followed by a global average pooling layer, a fully connected layer with 1024 node and ‘ReLU’ activation, a fully connected layer with 2 node and ‘softmax’ activation. The identified checkboxes and their contents are stored as label-value pairs, where the label identifies the checkbox and the value identifies the value of the checkbox, which can be either Yes, No, or No checkbox found.