Data structure generation for tabular information in scanned images

A computer program product for generating a data structure representing tabular information in a scanned image, said computer program product comprising a computer readable storage medium having program instructions embodied therein, the program instructions being executable by a computing apparatus to cause the computing apparatus to:

• store image data representing a scanned image of a table;
• process the image data to identify positions of characters and lines in the image;
• map locations in the image of information cells, each containing a set of said characters, in dependence on said positions;
• for each cell, determine cell attribute values, dependent on said locations, for a predefined set of cell attributes and supply the attribute values as inputs to a machine-learning model trained to pre-classify cells as header cells or data cells in dependence on cell attribute values;
• apply predefined rules to the pre-classified cells to identify a corner data cell marking a transition between a region containing data cells and edge regions containing header cells in the image, and define row and column header cells in dependence on location of cells in said edge regions;
• for each of the set of row header cells and the set of column header cells, define a hierarchy tree, indicating hierarchical structure of the header cells, in dependence on relative locations of those cells in the image;
• use the hierarchy trees to generate a data structure mapping each data cell to corresponding row and column header cells in said hierarchical structure;
• and
• store the data structure to provide a queryable representation of said table.

Apparatus for generating a data structure representing tabular information in a scanned image, the apparatus comprising:

• memory for storing image data representing a scanned image of a table;
• image processing logic adapted to process the image data to identify positions of characters and lines in the image;
• cell-detection logic adapted to map locations in the image of information cells, each containing a set of said characters, in dependence on said positions;
• cell pre-classification logic adapted, for each cell, to determine cell attribute values, dependent on said locations, for a predefined set of cell attributes and to supply the attribute values as inputs to a machine-learning model trained to pre-classify cells as header cells or data cells in dependence on cell attribute values;
• cell analysis logic adapted to apply predefined rules to the pre-classified cells to identify a corner data cell marking a transition between a region containing data cells and edge regions containing header cells in the image, and to define row and column header cells in dependence on location of cells in said edge regions;
• hierarchy extraction logic adapted, for each of the set of row header cells and the set of column header cells, to define a hierarchy tree, indicating hierarchical structure of the header cells, in dependence on relative locations of those cells in the image;
• data mapping logic adapted to use the hierarchy trees to generate a data structure mapping each data cell to corresponding row and column header cells in said hierarchical structure, and to store the data structure in said memory to provide a queryable representation of said table.