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首页> 外文期刊>電子情報通信学会技術研究報告 >Segmentation-free MRF Recognition Method in Combination with P2DBMN-MQDF for Online Handwritten Cursive Word
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Segmentation-free MRF Recognition Method in Combination with P2DBMN-MQDF for Online Handwritten Cursive Word

机译：结合P2DBMN-MQDF的在线手写草书词无分割MRF识别方法

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摘要

This paper describes an online handwritten English cursive word recognition method using a segmentation-free Markov random field (MRF) model in combination with an offline recognition method which uses pseudo 2D bi-moment normalization (P2DBMN) and modified quadratic discriminant function (MQDF). It extracts feature points along the pen-tip trace from pen-down to pen-up and uses the feature point coordinates as unary features and the differences in coordinates between the neighboring feature points as binary features. Each character is modeled as a MRF and word MRFs are constructed by concatenating character MRFs according to a trie lexicon of words during recognition. Our method expands the search space using a character-synchronous beam search strategy to search the segmentation and recognition paths. This method restricts the search paths from the trie lexicon of words and preceding paths, as well as the lengths of feature points during path search. Moreover, we combine it with a P2DBMN-MQDF recognizer that is widely used for Chinese and Japanese character recognition.
机译：本文描述了一种在线手写英语草书单词识别方法，该方法使用无分段马尔可夫随机域（MRF）模型与使用伪二维二维矩归一化（P2DBMN）和改进的二次判别函数（MQDF）的离线识别方法相结合。它从笔尖向下到笔尖沿笔尖轨迹提取特征点，并将特征点坐标用作一元特征，并将相邻特征点之间的坐标差用作二元特征。将每个字符建模为MRF，并通过在识别过程中根据单词的字典来串联字符MRF来构造单词MRF。我们的方法使用字符同步波束搜索策略扩展了搜索空间，以搜索分段和识别路径。此方法限制单词和前面路径的字典字典的搜索路径，以及路径搜索过程中特征点的长度。此外，我们将其与广泛用于中文和日语字符识别的P2DBMN-MQDF识别器相结合。

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