Abstract: In an approach, a processor performs document layout analysis on a document generating a plurality of textual regions; extracts characteristics from each of the plurality of textual regions and associates the respective characteristics to the respective textual region as metadata; classifies each of the plurality of textual regions as an optical character recognition (OCR) region, non-OCR valuable region, or non-OCR non-valuable region using a classifier; performs OCR on each OCR region generating an OCR output; identifies associated constant OCR data from a constant OCR data repository for each non-OCR valuable region; merges the associated constant OCR data with the OCR output generating a complete OCR data for the received document; performs data extraction on the complete OCR data to identify data fields and key-value pairs generating extracted data; and determines whether the extracted data is valid based on a set of rules.

Abstract: Disclosed embodiments provide techniques for monitoring and evaluating the effectiveness of key value pairs (KVPs) used in a document processing system. In embodiments, KVPs are obtained from multiple extractors of a document processing system. A score is computed for the KVPs by computing an effectiveness metric for each KVP from the multiple KVPs. In response to the computed score being below a predetermined threshold, a model retraining process is performed to generate a new set of KVP extractors, and provide the new set of KVPs to the document processing system.

Abstract: Disclosed embodiments provide techniques for monitoring and evaluating the effectiveness of key value pairs (KVPs) used in a document processing system. In embodiments, KVPs are obtained from multiple extractors of a document processing system. A score is computed for the KVPs by computing an effectiveness metric for each KVP from the multiple KVPs. In response to the computed score being below a predetermined threshold, a model retraining process is performed to generate a new set of KVP extractors, and provide the new set of KVPs to the document processing system.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) that merges a document comprising multiple pages into a single document image. The program code processes the single document image to identify structural elements and textual content. The program code compares the structural elements of the single document image to other structural elements of a group of document templates stored in a database to identify a subset of the group of documents templates with a threshold number of similarities to the single document image. The program code generates, from the single document image, a graph structure representing the document, where the graph structure comprises visual information and connections related to the structural elements and concepts comprising the textual content. The program code uses the structure to identify a document template that is a closest match to the document.

Abstract: A computer-implemented method for text block segmentation includes determining a first text block segmentation pattern utilized to generate a segmented text block based, at least in part, on a comparison of semantic information associated with the segmented text block and a plurality of predefined types of text block segmentation patterns indicated by a graph; calculating a first degree of confidence in a size of the segmented text block based, at least in part, on comparing semantic entities associated with the segmented text block with semantic entities indicated by leaf nodes stemming from a first non-leaf node included in the graph and representative of the first type of text block segmentation pattern; and determining that the size of the segmented text block is non-optimal based on the calculated degree of confidence in the size of the segmented text block being below a predetermined threshold.