A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings

  • Authors:

  • Javad Sadri;Ching Y. Suen;Tien D. Bui

  • Affiliations:

  • CENPARMI (Center for Pattern Recognition and Machine Intelligence), Computer Science Department, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, Que., Canada H3G1M8;CENPARMI (Center for Pattern Recognition and Machine Intelligence), Computer Science Department, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, Que., Canada H3G1M8;Computer Science and Software Engineering Department, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, Que., Canada H3G1M8

  • Venue:
  • Pattern Recognition
  • Year:
  • 2007

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Abstract

For the first time, a genetic framework using contextual knowledge is proposed for segmentation and recognition of unconstrained handwritten numeral strings. New algorithms have been developed to locate feature points on the string image, and to generate possible segmentation hypotheses. A genetic representation scheme is utilized to show the space of all segmentation hypotheses (chromosomes). For the evaluation of segmentation hypotheses, a novel evaluation scheme is introduced, in order to improve the outlier resistance of the system. Our genetic algorithm tries to search and evolve the population of segmentation hypotheses, and to find the one with the highest segmentation/recognition confidence. The NIST NSTRING SD19 and CENPARMI databases were used to evaluate the performance of our proposed method. Our experiments showed that proper use of contextual knowledge in segmentation, evaluation and search greatly improves the overall performance of the system. On average, our system was able to obtain correct recognition rates of 95.28% and 96.42% on handwritten numeral strings using neural network and support vector classifiers, respectively. These results compare favorably with the ones reported in the literature.