An Information Extraction Model for Unconstrained Handwritten Documents

In this paper, a new information extraction system by statistical shallow parsing in unconstrained handwritten documents is introduced. Unlike classical approaches found in the literature as keyword spotting or full document recognition, our approch relies on a strong and powerful global handwriting model. A entire text line is considered as an indivisible entity and is modeled with Hidden Markov Models. In this way, text line shallow parsing allows fast extraction of the relevant information in any document while rejecting at the same time irrelevant information. First results are promising and show the interest of the approach.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Simon Thomas, Clement Chatelain, Laurent Heutte, Thierry Paquet
• Keywords: Computer Vision, ICPR 2010, Shallow Parsing, Statistical Shallow Parsing, Text Line