Text line localization and segmentation is an important preprocessing stage in the context of document image analysis. Text lines must be localized first and then segmented in the logical order. The final recognition results are highly dependent on the results of text line segmentation. Historical documents have free form handwritten text and pose a great challenge for text line segmentation. The presence of connected and overlapping characters make the segmentation task more challenging. Smearing techniques have been conventionally used for the purpose of text line localization. In this paper, performance analysis of various smearing techniques is carried out on the text line localization of Kannada historical scripts.

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Smearing Techniques Run Length Smoothing Algorithm Binary Transition Count Map Adaptive Local Connectivity Map Touching / Overlapping Components