Paper
28 January 2008 A generic method for structure recognition of handwritten mail documents
Author Affiliations +
Proceedings Volume 6815, Document Recognition and Retrieval XV; 68150W (2008) https://doi.org/10.1117/12.766477
Event: Electronic Imaging, 2008, San Jose, California, United States
Abstract
This paper presents a system to extract the logical structure of handwritten mail documents. It consists in two joined tasks: the segmentation of documents into blocks and the labeling of such blocks. The main considered label classes are: addressee details, sender details, date, subject, text body, signature. This work has to face with difficulties of unconstrained handwritten documents: variable structure and writing. We propose a method based on a geometric analysis of the arrangement of elements in the document. We give a description of the document using a two-dimension grammatical formalism, which makes it possible to easily introduce knowledge on mail into a generic parser. Our grammatical parser is LL(k), which means several combinations are tried before extracting the good one. The main interest of this approach is that we can deal with low structured documents. Moreover, as the segmentation into blocks often depends on the associated classes, our method is able to retry a different segmentation until labeling succeeds. We validated this method in the context of the French national project RIMES, which proposed a contest on a large base of documents. We obtain a recognition rate of 91.7% on 1150 images.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aurélie Lemaitre, Jean Camillerapp, and Bertrand Coüasnon "A generic method for structure recognition of handwritten mail documents", Proc. SPIE 6815, Document Recognition and Retrieval XV, 68150W (28 January 2008); https://doi.org/10.1117/12.766477
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Cited by 8 scholarly publications.
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KEYWORDS
Image segmentation

Image resolution

Binary data

Feature extraction

Image analysis

Optical character recognition

Silicon

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