Publication: Printed Thai character recognition by genetic algorithm
Issued Date
2007-12-01
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2-s2.0-38049027868
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. Vol.6, (2007), 3354-3359
Suggested Citation
Chomtip Pornpanomchai, Montri Daveloh Printed Thai character recognition by genetic algorithm. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. Vol.6, (2007), 3354-3359. doi:10.1109/ICMLC.2007.4370727 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/24380
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Printed Thai character recognition by genetic algorithm
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Abstract
This research applied a genetic algorithm in the pattern of cellular automata and through Conway's rules of the game of life, to generate a system of printed Thai character recognition. The system consisted of two main parts, namely, recognition training and recognition testing. The printed character images fed to the first part were derived from standard character patterns widely used in a computer currently totalling 72, 864 characters. As for the images used for recognition testing, they were captured from a computer screen and stored in BMP pattern, amounting to 1,015 characters. The findings in this research revealed that the database used was of large size and data was transformed from a table frame of 64 × 64 pixels to be stored in the form of bit strings. A table size of 64 × 64 pixels was used to enable a wide variety of distribution patterns of the stable state of each character, making its identity more obvious. This, of course, caused a modification process in each generation till the final generation which took a long time while the database was used to represent the population of the final generation of each character must be large enough for the bit string used to represent these characters. This would enable the system to recognize a character based on its frequency with the largest number of those bit string patterns. Out of 1,015 printed Thai characters tested, it was found that the system could recognize (accept) 986 characters or 97.14%, while rejecting 6 characters or 0.59% and misrecognizing 23 characters or 2.27%. The recognition speed is 85 seconds per character on the average. ©2007 IEEE.