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Archaeological departments throughout the world have undertaken massive digitization projects to digitize their historical document corpus. In order to provide worldwide visibility to these historical documents residing in the digital libraries, a character recognition system is an inevitable tool.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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Edge detection from handwritten text documents, particularly of Kannada language, is a challenging task. Kannada has a huge character set, amounting to 17,340 character combinations. Moreover, in handwritten Kannada, the character strokes are highly variable in size and shape due to varying handwriting styles. This chapter presents a solution for edge detection of Kannada handwritten documents. Sobel edge detection method, which efficiently enhances the image contrast and detects the character edges, is proposed. Experimentation of this edge detection approach yielded high F-measure and global contrast factor values.