Efficient and Intelligent Off-Line Handwritten Recognition Systems
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The major contributions of the thesis are listed below: Feed forward neural architecture is identified as the most suitable classifier for recognizing handwritten English characters. A new zonal feature extraction, called diagonal feature extraction is proposed. Hybridization of feature is investigated to enhance recognition accuracy. The best hybrid feature set is identified. Novel training strategy for neural classifier is proposed to improve the average and worst case recognition accuracies. A comprehensive CRS for Mixed handwritten English alphabets is built. A general algorithm for design...
The major contributions of the thesis are listed below: Feed forward neural architecture is identified as the most suitable classifier for recognizing handwritten English characters. A new zonal feature extraction, called diagonal feature extraction is proposed. Hybridization of feature is investigated to enhance recognition accuracy. The best hybrid feature set is identified. Novel training strategy for neural classifier is proposed to improve the average and worst case recognition accuracies. A comprehensive CRS for Mixed handwritten English alphabets is built. A general algorithm for designing CRS is developed. A methodology to validate the performance of the designed CRS is evolved and illustrated. This thesis presents a systematic procedure for designing and developing a high accuracy character recognition system for handwritten English characters.
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