Efficient Handwritten Numeral Recognition System Using Leaders of Separated Digit and RBF Network

Abstract

Abstract. In this paper an efficient method has been proposed to classify handwritten numerals using leader algorithm and Radial Basis Function network. Handwritten numerals are represented in matrix form and clusters with leaders are formed for each row of each digit separately. Every leader is with single target digit. Duplication patterns are avoided from the cluster leaders by combining those in a single pattern with target vectors having corresponding bits in on mode. Now resultant target vectors are with 10 bits corresponding to the number of digits considered for classification. Constructed leaders are trained using Radial Basis Function network. Experimental results show that the minimum number of patterns are enough for training compared to total patterns and it has been observed that convergency is fast during training. Also the number of resultant leaders after avoiding duplication patterns are less and the number of bits in each resultant pattern is 12.

A Numeral Script Identification from a Multi-lingual Printed Document Image

“…The task of Multilingual handwritten numeral recognition using a robust deep network joint with transfer learning is presented by Amirreza Fateh [19] and the proposed system was tested with six different languages. Hangairulappan and others [17] reported work on isolated digits of Handwritten Numeral Recognition System. Shrey Malvi [20] claims the work on Variable Length Digit Recognition system for Gujarati Language.…”