Handwritten Kannada Vowels and English Character Recognition System

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

WRITER IDENTIFICATION BY TEXTURE ANALYSIS BASED ON KANNADA HANDWRITING

Writer identification problem is one of the important area of research due to its various applications and is a challenging task. The major research on writer identification is based on handwritten English documents with text independent and dependent. However, there is no significant work on identification of writers based on Kannada document. Hence, in this paper, we propose a text-independent method for off-line writer identification based on Kannada handwritten scripts. By observing each individual’s handwriting as a different texture image, a set of features based on Discrete Cosine Transform, Gabor filtering and gray level co-occurrence matrix, are extracted from preprocessed document image blocks. Experimental results demonstrate that the Gabor energy features are more potential than the DCTs and GLCMs based features for writer identification from 20 people

Directional Discrete Cosine Transform for Handwritten Script Identification

Authors' copy - ICDAR International Conference on Document Analysis and Recognition (2013), Washington DC, USAInternational audienceThis paper presents directional discrete cosine transforms (D-DCT) based word level handwritten script identification. The conventional discrete cosine transform (DCT) emphasizes vertical and horizontal energies of an image and de-emphasizes directional edge information, which of course plays a significant role in shape analysis problem, in particular. Conventional DCT however, is not efficient in characterizing the images where directional edges are dominant. In this paper, we investigate two different methods to capture directional edge information, one by performing 1D-DCT along left and right diagonals of an image, and another by decomposing 2D-DCT coefficients in left and right diagonals. The mean and standard deviations of left and right diagonals of DCT coefficients are computed and are used for the classification of words using linear discriminant analysis (LDA) and K-nearest neighbour (K-NN). We validate the method over 9000 words belonging to six different scripts. The classification of words is performed at bi-scripts, tri-scripts and multi-scripts scenarios and accomplished the identification accuracies respectively as 96.95%, 96.42% and 85.77% in average

A Zone Based Character Recognition Engine for Kannada and English Scripts

AbstractIn this paper, an Optical Character Recognition engine for Kannada and English character recognition is proposed based on zone features. The zone is one of the old concepts in case of document image analysis research. But this method is good in case of Kannada and English character recognition. The total of 2800 Kannada consonants and 2300 English lowercase alphabets sample images are classified based on the SVM classifier. All preprocessed images are normalized into 32 x 32 dimensions, it is optimum. Then the preprocessed image is divided into 64 zones of non overlapping and zone based pixel density is calculated for each of the 64 zones, there by generating 64 features. These features are fed to the SVM classifier for classification of character images. To test the performance of an algorithm 2 fold cross validation is used. The average recognition accuracy of 73.33% and 96.13% is obtained for Kannada consonants and English lowercase alphabets respectively. Further the average percentage of recognition accuracy of 83.02% is obtained for mixture input of both Kannada and English characters. The recognition accuracy obtained for Kannada consonants is low, because most of the characters are similar in shape. Hence, one may need to add some more dominating features to discriminating the characters. In this direction, the work is in progress. It is an initial attempt for mixture of Kannada and English characters recognition with single algorithm. The novelty of the algorithm is independent of thinning and slant of the characters