Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Attention-Feedback Based Robust Segmentation of Online Handwritten Isolated Tamil Words
ACM Transactions on Asian Language Information Processing (TALIP)
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Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten recognition system. For the segmentation of unconstrained Oriya handwritten text into individual characters, a water-reservoir-concept based scheme is proposed in this paper. Here, at first, the text image is segmented into lines, and then lines are segmented into individual words, and words are segmented into individual characters. For line segmentation the document is divided into vertical stripes. Analyzing the heights of the water reservoirs obtained from different components of the document, the width of a stripe is calculated. Stripe-wise horizontal histograms are then computed and the relationship of the peak-valley points of the histograms is used for line segment. Based on vertical projection profile and structural features of Oriya characters, text lines are segmented into words. For character segmentation, at first, isolated and connected (touching) characters in a word are detected. Using structural, topological and water-reservoir-concept based features touching characters of the word are then segmented.