Form Field and Filled-in Data Extraction from Printed Documents

[[abstract]]Form document analysis is one of the most essential tasks in document analysis and recognition. The problems of form fields and filled-in data extraction are two important parts of form document analysis. For form field extraction, the first major task was to classify the preprinted text, lines, check boxes, text boxes and the tables of a form. This thesis proposes a method which based on direction-invariant global structural features and directional dependant structural features to classify the form fields, and then extract the filled-in spaces in a form document. Since tables can contain both name fields and data fields, for the second task, we used a method based on horizontal and vertical color histogram distribution features to segment the fields and extract the data fields. For filled-in data extraction, we propose a method which based on Run-based algorithm and the idea of interpolation to detect the character strokes overlapped by printed form frame and reconstruct the broken strokes after removing the frame line. The experimental results on different types of form documents showed a 99% recognition rate on form fields extraction, and a 91% successful filled-in data extraction rate was achieved.