Abstract: For table structure recognition in document processing, a methodology can use 2-click human interaction to define bounding box of the table and convert OCR data into n-dim positioning for column-wise and row-wise assignment. An adaptive filter can identify and assign column-like morphologies in tables. An adaptive cycle based column/row assignment can select best methodology for each table. Column overlap and merge detection can use post-processing for complex table structures. Data storage and feedback mechanisms can be used for adaptive template predictions.

Abstract: A computer system extracts contender values as positively associated with a pre-defined value from a compilation of one or more electronically stored semi-structured document(s) and/or one or more electronically stored unstructured document(s). The computer system performs a multi-dimensional analysis to narrow the universe of contender values from all words on a page of the compilation to the contender value(s) with the highest likelihood of being associated with the pre-defined value. The system's platform allows every user of the system to customize the system according to the user's needs. Various aspects can enable users to mine document stores for information that can be charted, graphed, studied, and compared to help make better decisions.

Abstract: A computer system extracts contender values as positively associated with a pre-defined value from a compilation of one or more electronically stored semi-structured document(s) and/or one or more electronically stored unstructured document(s). The computer system performs a multi-dimensional analysis to narrow the universe of contender values from all words on a page of the compilation to the contender value(s) with the highest likelihood of being associated with the pre-defined value. The system's platform allows every user of the system to customize the system according to the user's needs. Various aspects can enable users to mine document stores for information that can be charted, graphed, studied, and compared to help make better decisions.

Abstract: A computer system extracts contender values as positively associated with a pre-defined value from a compilation of one or more electronically stored semi-structured document(s) and/or one or more electronically stored unstructured document(s). The computer system performs a multi-dimensional analysis to narrow the universe of contender values from all words on a page of the compilation to the contender value(s) with the highest likelihood of being associated with the pre-defined value. The system's platform allows every user of the system to customize the system according to the user's needs. Various aspects can enable users to mine document stores for information that can be charted, graphed, studied, and compared to help make better decisions.

Abstract: A computer system extracts contender values as positively associated with a pre-defined value from a compilation of one or more electronically stored semi-structured document(s) and/or one or more electronically stored unstructured document(s). The computer system performs a multi-dimensional analysis to narrow the universe of contender values from all words on a page of the compilation to the contender value(s) with the highest likelihood of being associated with the pre-defined value. The system's platform allows every user of the system to customize the system according to the user's needs. Various aspects can enable users to mine document stores for information that can be charted, graphed, studied, and compared to help make better decisions.