A Survey on Offline-Methods of Character Segmentation
B. Thakkar Nirav, Kandarp Pandya, Juhi Kaneria, Ruchita Tailor, Kruti Dangarwala · 2012
Character segmentation is the critical area of the Optical Character Recognition process. The higher recognition rates for isolated characters as compared to those obtained for words and connected character strings illustrate this fact. This paper provides a review of various techniques of character segmentation, which are classified mainly into four classes. In classical approach the input image is partitioned into sub images, which are then classified. The operation of attempting to decompose the image into classifiable units is called “dissection”. In the second class of method, the dissection method is avoided and the image is segmented either explicitly by classification of pre specified windows, or implicitly by classification of subsets of spatial features collected from the image. The third strategy is hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these sub images. Finally, holistic approaches avoid segmentation by recognizing entire character strings as units. Keywords—segmentation, contextual method, graphemes, Hidden Markov Models, holistic recognition, Optical character recognition, recognition-based segmentation and survey