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清华大学学报(自然科学版)  2015, Vol. 55 Issue (8): 873-877,883    
  电子工程 本期目录 | 过刊浏览 | 高级检索 |
针对无切分维吾尔文文本行识别的字符模型优化
姜志威, 丁晓青, 彭良瑞
清华大学 电子工程系, 智能技术与系统国家重点实验室, 清华信息科学与技术国家实验室, 北京 100084
Character model optimization for segmentation-free Uyghur text line recognition
JIANG Zhiwei, DING Xiaoqing, PENG Liangrui
State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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摘要 基于隐含Markov模型(hidden Markov model, HMM)的无切分文本行识别方法能够利用概率图的思想, 同步完成文本行图像的切分与识别, 避免因字符预切分失败而导致的识别错误, 但对字符模型的设计与训练要求很高, 并且在多字体融合问题中难以提高模型泛化性能。该文通过分析模型状态在图像层面的聚类意义, 先提出基于观测合理聚类的模型结构优化方法, 再提出结构与参数相结合的字符模型优化策略, 最后将其应用于多字体维吾尔文文本行的无切分识别系统。实验结果表明, 该方法能够改善模型的状态分配合理性, 并且在多字体融合问题中提高了模型泛化性能和状态利用效率。
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姜志威
丁晓青
彭良瑞
关键词 信息处理文字识别隐含Markov模型统计学习维吾尔文    
Abstract:A text line recognition method was developed without pre-segmentation using a hidden Markov model (HMM) for simultaneously segmenting and recognizing text line images. The algorithm uses a probability graph to reduce recognition error from failed pre-segmentation results. However, the HMM design and training is complicated and the HMM generalization performance can not be easily improved in multi-font texts. Therefore, a character model optimization method with reasonably clustered observations was developed based on the most common HMM state in images. Then, a method was developed to optimize the model structure and parameters together for a multi-font Uyghur text line recognition system. Tests show that this method improves the state allocation, the generalization performance and the state efficiency of the character model for multi-font texts.
Key wordsinformation processing    character recognition    hidden Markov model (HMM)    statistical learning    Uyghur
收稿日期: 2015-04-15      出版日期: 2015-09-30
ZTFLH:  TP391.4  
通讯作者: 丁晓青,教授,E-mail:dingxq@tsinghua.edu.cn     E-mail: dingxq@tsinghua.edu.cn
引用本文:   
姜志威, 丁晓青, 彭良瑞. 针对无切分维吾尔文文本行识别的字符模型优化[J]. 清华大学学报(自然科学版), 2015, 55(8): 873-877,883.
JIANG Zhiwei, DING Xiaoqing, PENG Liangrui. Character model optimization for segmentation-free Uyghur text line recognition. Journal of Tsinghua University(Science and Technology), 2015, 55(8): 873-877,883.
链接本文:  
http://jst.tsinghuajournals.com/CN/  或          http://jst.tsinghuajournals.com/CN/Y2015/V55/I8/873
  图1 HMM 基本原理示意图
  图2 结构与参数相结合的字符模型优化策略
  表1 THOCR-Uy360数据库的5种字体
  表2 各个系统的CRA
  表3 各个系统的建模效率
  表3 各个系统的建模效率
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