IJIPVS

Handwritten Kannada Vowels and English Character Recognition Handwritten Kannada Vowels and English Character Recognition System

---

---

Abstract

In this paper, a zone based features are extracted from handwritten Kannada Vowels and English uppercase Character images for their recognition. A Total of 4,000 handwritten Kannada and English sample images are collected for classifications. The collected images are normalized into 32 x 32 dimensions. Then the normalized images are divided into 64 zones and their pixel densities are calculated, generating a total of 64 features. These 64 features are submitted to KNN and SVM classifiers with 2 fold cross validation for recognition of the said characters. The proposed algorithm works for individual Kannada vowels, English uppercase alphabets and mixture of both the characters. The recognition accuracy of 92.71% for KNN and 96.00% for SVM classifiers are achieved in case of handwritten Kannada vowels and 97.51% for KNN and 98.26% for SVM classifiers are obtained in case of handwritten English uppercase alphabets. Further, the recognition accuracy of 95.77% and 97.03% is obtained for mixed characters (i.e. Kannada Vowels and English uppercase alphabets). Hence, the proposed algorithm is efficient for the said characters recognition. The proposed algorithm is independent of thinning and slant of the characters and is the novelty of the proposed work.

Recommended Citation

[1] B.V.Dhandra, Mallikarjun Hangarge, Gururaj Mukarambi, ”Spatial Features for Handwritten Kannada and English Character Recognition System”, Special Issue on RTIPPR-10, International Journal of Computer Applications (IJCA), pp.146-150, Aug-2010. [2] P. Phokharatkul, K. Sankhuangaw, S. Somkuarnpanit, S. Phaiboon, and C. Kimpan, “Off-Line Hand Written Thai Character Recognition using Ant-Miner Algorithm”, World Academy of Science, Engineering and Technology 8, Year-2005. [3] R.M. Suresh, "Printed and Handwritten Tamil Characters Recognition Using Fuzzy Technique", Proceedings of the International Multi Conference of Engineers and Computer Scientists 2008 Vol II, MECS 2008, 19-21 March, 2008, Hong Kong. [4] Teng Long1, Lian-Wen Jin, Li-Xin Zhen , Jian-Cheng Huang, "one Stroke Cursive Character Recognition Using Combination of Directional and Positional Features", ICASSP-2005,IEEE,P.No v 449 - 452. [5] Ertugrul Saatci and Vedat Tavsanoglu, "Multiscale Handwritten Character Recognition Using CNN Image Filters", IEEE-2002. [6] Velappa Ganapathy, and Kok Leong Liew, "Handwritten Character Recognition Using Multiscale Neural Network Training Technique", World Academy of Science, Engineering and Technology, 39 2008.[7] Dayashankar Singh, Sanjay Kr.Singh, Maitreyee Dutta, "Hand Written Character Recognition Using Twelve Directional Feature Input and Neural Network", International Journal of Computer Applications (0975 – 8887)Volume 1 – No. 3,2010. [8] Dharamveer Sharma, Puneet Jhajj, "Recognition of Isolated Handwritten Characters in Gurumukhi Script", International Journal of Computer Applications, Volume 4, No.8, August 2010. [9] S.Arora, D.Bhattacharjee, M.Nasipuri , D.K.Basu , M.Kundu, "Application of Statistical Features in Handwritten Devnagari Character Recognition", International Journal of Recent Trends in Engineering, Vol 2, No. 2, Academy Publisher, November 2009. [10] B.V.Dhandra, Mallikarjun Hangarge, Gururaj Mukarambi, "Spatial Features for Multi-Font/Multi-Size Kannada Numerals Recognition”, International Conference on Communication, Computation, Control and Nano Technology (ICN-2010), Bhalki, Bidar, Karnataka. [11] B.V.Dhandra, Gururaj Mukarambi, Mallikarjun Hangarge,” Zone Based Features for Handwritten and Printed Mixed Kannada Digits Recognition”, International Conference on VLSI, COMMUNICATION & INSTRUMENTATION, ICVCI – 2011, Held at Kottayam, Kerala, India.