Online Handwritten Cursive Word Recognition by Combining Segmentation-Free and Segmentation-Based Methods

This paper describes an online handwritten cursive word recognition approach by combining segmentation-free and segmentation-based methods. To search the optimal segmentation and recognition path as the recognition result, we can attempt two methods: segmentation-free and segmentation-based, where we expand the search space using a character-synchronous beam search strategy. The probable search paths are evaluated by integrating character recognition scores with geometric characteristics of the character patterns in a Conditional Random Field (CRF) model. We make a comparison between online handwritten cursive word recognition using segmentation-free method and that using segmentation-based method, and then attempt combining the two methods to improve performance. Our methods restrict the search paths from the trie lexicon of words and preceding paths during path search. We show this comparison on a publicly available dataset (IAM-OnDB).