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| Abstract: | Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu |
| Description: | National Conference on Indian Language Computing, Kochi, Feb 19-20, 2011 |
| URI: | http://dyuthi.cusat.ac.in/purl/4191 |
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| Handwritten Cha ... ndian Scripts A Review.pdf | (189.7Kb) |
| Abstract: | Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification |
| Description: | Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on |
| URI: | http://dyuthi.cusat.ac.in/purl/4196 |
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| Offline Handwri ... n Chain Code Histogram.pdf | (1.324Mb) |
| Abstract: | In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets |
| Description: | I.J. Image, Graphics and Signal Processing, 2013, 4, 53-59 |
| URI: | http://dyuthi.cusat.ac.in/purl/4204 |
| Files | Size |
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| A System for Of ... rs in Malayalam Script.pdf | (535.2Kb) |
| Abstract: | This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective |
| Description: | Procedia Engineering 30 (2012) 598 – 605 |
| URI: | http://dyuthi.cusat.ac.in/purl/4195 |
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| Unconstrained H ... tor Machine Classifier.pdf | (442.7Kb) |
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