Handwritten Text Segmentation using Average Longest Path Algorithm

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Title: Handwritten Text Segmentation using Average Longest Path Algorithm

Authors: Dhaval Salvi, Jun Zhou, Jarrell Waggoner, and Song Wang

Abstract: Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different hand-writing styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraint on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using setof lexicons to guide and correct the segmentation and recognition. This approach may fail when the lexicon domain is insufficient. In this paper, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the characterize and the number of characters in a word. Specifically, the proposed method finds the text segmentation with the maximum average likeliness for the resulting characters. For this purpose, we use a graph model that describes the possible locations for segmenting neighboring characters, and we then develop an average longest path algorithm to identify the globally optimal segmentation. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the proposed method against an existing text segmentation algorithm that uses dynamic programming.   

Publish Year: 2013

Published in: WACV – IEEE

موضوع: تشخصی دست خط (Handwritten Text Recognition)

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