Word segmentation is the most critical pre-processing step for any handwritten document recognition and/or retrieval system. When the writing style is unconstrained (written in a natural manner), recognition of individual components may be unreliable, so they must be grouped together into word hypotheses before recognition algorithms can be used. This paper describes a gap metrics based machine learning approach to separate a line of unconstrained handwritten text into words. Our approach uses a set of both local and global features, which is motivated by the ways in which human beings perform this kind of task. In addition, in order to overcome the disadvantage of different distance computation methods, we propose a combined distance measure computed using three different methods. The classification is done by using a three-layer neural network. The algorithm is evaluated using an unconstrained handwriting database that contains 50 pages (1026 line, 7562 words images) handwritten documents. The overall accuracy is 90.8%, which shows a better performance than a previous method.
Huang and Srihari REF described an approach to separate a line of handwritten text to words.
Word segmentation of off-line handwritten documents
Electronic Imaging
Engineering Computer Science