Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features

Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features

Thippeswamy G., Chandrakala H. T.
Copyright: © 2020 |Pages: 15
DOI: 10.4018/IJNCR.2020070101
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Archaeological departments throughout the world have undertaken massive digitization projects to digitize their historical document corpus. In order to provide worldwide visibility to these historical documents residing in the digital libraries, a character recognition system is an inevitable tool. Automatic character recognition is a challenging problem as it needs a cautious blend of enhancement, segmentation, feature extraction, and classification techniques. This work presents a novel holistic character recognition system for the digitized Estampages of Historical Handwritten Kannada Stone Inscriptions (EHHKSI) belonging to 11th century. First, the EHHKSI images are enhanced using Retinex and Morphological operations to remove the degradations. Second, the images are segmented into characters by connected component labeling. Third, LBP features are extracted from these characters. Finally, decision tree is used to learn these features and classify the characters into appropriate classes. The LBP features improved the performance of the system significantly.
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Literature Review

Achieving high recognition accuracy on historical handwritten characters is a challenging problem, for which many different solutions have been published in the literature till date. For the Historical handwritten Korean documents (Min et al; 2004) proposed a recognition based character segmentation. LDA classifier was used to perform classification based on geometric features and context information. 92.98% recognition rate was reported. (Vamvakas et al; 2008) proposed a classification system based on SVM and RBF kernel for Historical documents. The system was trained on pixel density of character zones and could achieve an accuracy of 95%. In (G Vamvakas; 2009) features based on mass of each sub image of the characters were extracted and fed to a hierarchical classifier to recognize Greek Historical Documents with an accuracy of 94%. A decision tree approach based on correlation coefficient character grouping was employed by (Sastry et al; 2010) for classification of Telugu characters extracted from palm leaves with an accuracy of 93%. (Raj Kumar et al; 2012) proposed a self-adaptive learning SVM classifier based on time domain and frequency domain features. The method achieved 94% accuracy in recognition of 8th century Tamil consonants.

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