ISOLATED HANDWRITTEN LATIN AND DEVANAGARI NUMERAL RECOGNITION USING FOURIER DESCRIPTORS AND CORRELATION

Automatic recognition of handwritten numerals has importance in practical fields. In this communication we propose an efficient automatic recognition system for isolated handwritten Latin and Devanagari numerals. Fourier descriptors based features are extracted and are input to a feed forward backpropagation neural network for classification. The numeral recognition is also done by template matching classifier based on correlation metric. Using total 10000 training samples the proposed technique is tested on total 2360 handwritten Latin and Devanagari numerals extracted from dates present on bank cheques. The average recognition accuracy of 98.42% and 99.63% are obtained by using artificial neural network (ANN) classifier and template matching (TM) classifier respectively.
Handwritten numeral recognition Neural Networks Fourier Descriptors Correlation Devanagari numerals
R.V.KULKARNI P.N.VASAMBEKAR
Department of Technology,SHIVAJI University, Kolhapur-416004, India Department of Computer Science,Shivaji University, KLolhapur-416004, India
国际会议
3rd International Conference on Mechanical and Electrical Technology(ICMET2011) (2011第三届机械与电气技术国际会议)
大连
英文
719-723
2011-08-26(万方平台首次上网日期,不代表论文的发表时间)