Online Handwritten Cursive Word Recognition by Combining Segmentation-Free and Segmentation-Based Methods
Online Handwritten Cursive Word Recognition by Combining Segmentation-Free and Segmentation-Based Methods
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DOI：
10.1109/icfhr.2016.0084
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Bilan Zhu;Arti Shivram;V. Govindaraju;M. Nakagawa
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文献类型：
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作者：
Bilan Zhu;Arti Shivram;V. Govindaraju;M. Nakagawa
本文提出了一种结合无分割和基于分割的方法的在线手写体草书字识别方法。为了搜索最佳分割和识别路径作为识别结果，我们可以尝试两种方法：无分割和基于分割，其中我们使用字符同步波束搜索策略来扩展搜索空间。可能的搜索路径进行评估，通过整合字符识别分数与几何特征的字符模式中的条件随机场（CRF）模型。本文将基于分割的手写体草书识别方法与基于分割的手写体草书识别方法进行了比较，并尝试将两种方法结合起来提高识别性能。我们的方法限制了搜索路径从特里词典的单词和前面的路径在路径搜索。我们在一个公开的数据集（IAM-OnDB）上进行了比较。
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).