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Proceedings Paper

Document compression and reconstruction case study: official forms
Author(s): Ahmed H. Kandil
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Paper Abstract

The automation of the document storage and retrieval procedures has become an important issue in the restructuring of many institutes. Considering governmental agencies the official forms processing has attracted the attention of the decision-makers. Electronic storage of the documents is essential for improving the document processing. In this paper, the improvement of the official forms processing is considered. Each form consists of a static portion (the printed fields) and a dynamic portion (the handwritten filling). So, by splitting these forms into the described portions, the static portion will be discarded and the dynamic portion, which contains the information, is stored. The splitting of these forms depends on color differentiation of both portions. Only one static portion is kept as a reference for each form. This will result in a reduced size of the original document. Then, the resulting dynamic portions of document is binarized (background and foreground). This process is followed by the application of a lossless compression procedure (e.g. run length) on the binarized document. In order to retrieve any form, the compressed variable portion is expanded to its original size. Then, the hand written fields are aligned with the static fields in the reference documents. Then, the original document is reconstructed. The developed procedure was applied to the official identification card, more than 90% of the original size was reduced in the compression. This results in a more efficient use of the storage media. It will guarantee a faster transfer of the compressed file from a place to another using computer networks.

Paper Details

Date Published: 22 December 1999
PDF: 8 pages
Proc. SPIE 3967, Document Recognition and Retrieval VII, (22 December 1999); doi: 10.1117/12.373485
Show Author Affiliations
Ahmed H. Kandil, Cairo Univ. (Egypt)

Published in SPIE Proceedings Vol. 3967:
Document Recognition and Retrieval VII
Daniel P. Lopresti; Jiangying Zhou, Editor(s)

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