Weitere Kapitel dieses Buchs durch Wischen aufrufen
One of the major reasons for poor recognition rate in handwritten character recognition is the lack of unique features to represent handwritten characters. In this paper, an attempt is made to utilize the similarity already exist in different parts of the Gujarati characters. A novel feature extraction technique based on normalized cross correlation is proposed for handwritten Gujarati character recognition. An overall accuracy of 53.12%, 68.53%, and 66.43% is obtained using Naive Bayes classifier, linear and polynomial Support Vector Machine (SVM) classifiers, respectively, with the proposed feature extraction algorithm. Experimental results show significant contribution by proposed technique and improvement in recognition rate may be obtained by combining these features with some other significant features. One of the significant contributions of proposed work is the development of large and representative dataset of 20,500 isolated handwritten Gujarati characters.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
M. Chaudhary, G. Shikkenawis, S. K. Mitra, and M. Goswami, “Similar looking Gujarati printed character recognition using Locality Preserving Projection and artificial neural networks,” in International Conference on Emerging Applications of Information Technology, EAIT, 2012, pp. 153–156.
Pal, U., and B. B. Chaudhuri, “Indian script character recognition: a survey,” Pattern Recogniion., pp. 1887–1899, 2004.
N. Sharma, U. Pal, F. Kimura and S. Pal, “Recognition of off-line handwritten Devnagari characters using quadratic classifier”, Computer Vision, Graphics and Image Processing. Springer Berlin Heidelberg, 2006. 805–816.
U. Pal, N. Sharma, T.Wakabayashi, and F. Kimura, “Off-line handwritten character recognition of Devnagari script,” in Proc. 9th Conference on Document Analysis and Recognition, 2007, pp. 496–500.
U. Pal, S. Chanda, T. Wakabayashi, and F. Kimura, “Accuracy improvement of Devnagari character recognition combining SVM and MQDF,” in Proc. 11th Int. Conf. Frontiers Handwrit. Recognit., 2008, pp. 367–372. Dr. P. S. Deshpande, Latesh Malik, Sandhya Arora, “Fine classification recognition of handwritten devnagari characters with regular expressions minimum edit distance method”, JOURNAL OF COMPUTERS (2008). VOL. 3, NO. 5, MAY 2008.
U. Pal, T. Wakabayashi, and F. Kimura, “Comparative study of Devanagari handwritten character recognition using different features and classifiers,” in Proc. 10th Conf. Document Anal. Recognit., 2009, pp. 1111–1115.
Apurva A. Desai, “Gujarati handwritten numeral optical character reorganization through neural network”, Pattern Recognition 43 (2010) 2582–2589.
Mamta maloo, K.V. Kale, “Support vector machine based Gujarati numeral recognition”, International Journal on Computer Science and Engineering (IJCSE), ISSN: 0975-3397 Vol. 3 No. 7 July 2011.
Desai, Apurva A. “Support vector machine for identification of handwritten Gujarati alphabets using hybrid feature space.” CSI Transactions on ICT, pp. 1–7, 2015.
Ankit K. Sharma, Dipak M. Adhyaru, Tanish H. Zaveri, and Priyank B. Thakkar. “Comparative analysis of zoning based methods for Gujarati handwritten numeral recognition.”, 5th Nirma University International Conference on Engineering (NUiCONE), pp. 1–5. IEEE, 2015.
M. Goswami and S. Mitra, “Offline handwritten Gujarati numeral recognition using low-level strokes,” Int. J. Appl. Pattern Recognit., 2015.
N. Otsu, A threshold selection method from gray-level histograms, Automatica 11 (1975) 23–27.
Lewis, J. P. “Fast normalized cross-correlation.” Vision interface. Vol. 10. No. 1. 1995.
- A Novel Cross Correlation-Based Approach for Handwritten Gujarati Character Recognition
Ankit K. Sharma
Dipak M. Adhyaru
Tanish H. Zaveri
- Springer Singapore
Neuer Inhalt/© ITandMEDIA, Best Practices für die Mitarbeiter-Partizipation in der Produktentwicklung/© astrosystem | stock.adobe.com