Text Extraction from Gray Scale Historical Document Images Using Adaptive Local Connectivity Map

Authors:
Zhixin Shi;Srirangaraj Setlur;Venu Govindaraju
Affiliations:
State University of New York at Buffalo, Buffalo, NY;State University of New York at Buffalo, Buffalo, NY;State University of New York at Buffalo, Buffalo, NY
Venue:
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Year:
2005

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Abstract

This paper presents an algorithm using adaptive local connectivity map for retrieving text lines from the complex handwritten documents such as handwritten historical manuscripts. The algorithm is designed for solving the particularly complex problems seen in handwritten documents. These problems include fluctuating text lines, touching or crossing text lines and low quality image that do not lend themselves easily to binarizations. The algorithm is based on connectivity features similar to local projection profiles, which can be directly extracted from gray scale images. The proposed technique is robust and has been tested on a set of complex historical handwritten documents such as Newton's and Galileo's manuscripts. A preliminary testing shows a successful location rate of above 95% for the test set.