使用隐马尔可夫模型的阿萨姆在线手写数字识别系统

DAR '12 Pub Date : 2012-12-16 DOI:10.1145/2432553.2432573
G. S. Reddy, Bandita Sarma, R. Naik, S. Prasanna, C. Mahanta
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引用次数: 24

摘要

本工作描述了阿萨姆邦在线手写数字识别系统的开发。阿萨姆语的数字与孟加拉语的数字相同。收集了一个手写数字的大型数据库,并将其分为大小相等的两个部分。第一部分用于开发基于隐马尔可夫模型(HMM)的数字模型。(x, y)坐标及其一阶和二阶导数用作特征。数据库的第二部分针对模型进行测试,以评估性能。该数字识别系统的平均识别率为96.02%。在数字5和6之间观察到大量的混淆。新的距离特征被用作附加特征,模型被重新训练。数字5和6的性能从91.60%和95.40%提高到95.30%和94.90%。结果,混淆明显减少,平均识别性能提高到97.14%。
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Assamese online handwritten digit recognition system using hidden Markov models
This work describes the development of Assamese online handwritten digit recognition system. Assamese numerals are the same as the Bangla numerals. A large database of handwritten numerals is collected and partitioned into two parts of equal size. The first part is used for developing the Hidden Markov Models (HMM) based digit models. The (x, y) coordinates and their first and second time derivatives are used as features. The second part of the database is tested against the models to evaluate the performance. The digit recognition system provides an average recognition performance of 96.02%. A large amount of confusion is observed among the numerals 5 & 6. The new distance feature is used as an additional feature and the models are retrained. The performance for numeral 5 & 6 increases from 91.60% & 95.40% to 95.30% & 94.90%. As a result, the confusion reduces significantly and the average recognition performance increases to 97.14%.
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