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基于两方向动态时间规整的无分割手写汉字检测 被引量:3

Two-directional dynamic time warping based Chinese handwritten segmentation-free word spotting
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摘要 中文文本布局复杂、汉字种类多、书写随意性大,因而手写汉字检测是一个很有挑战的问题。针对上述问题,提出了一种无分割的手写中文文档字符检测的方法。该方法用SIFT定位文本中候选关键点,然后基于关键点位置和待查询汉字大小来确定候选字符的位置,最后用两个方向动态时间规整(dynamic time warping,DTW)算法来筛选候选字符。实验结果表明,该方法能够在无须将文本分割为字符的情况下准确找到待查询的汉字,并且优于传统的基于DTW字符检测方法。 Large variety of Chinese characters and handwriting styles and the complexity of Chinese handwritten documents layout lead a huge challenging for the Chinese handwriting word "spotting. This paper proposed a segmentation-free word spot- ting method for Chinese handwritten documents. Firstly, the method used the SIFT keypoint detector to locate the candidate keypoints in document images. Then it determined the candidate character regions by the keypoints' locations and the size of query word image. At last, it applied the two-directional dynamic time warping (DTW) to refine the candidate regions. The experimental results show that the proposed method can detect the query word in the document images with high mean average precision and the two-directional DTW outperforms the traditional DTW.
出处 《计算机应用研究》 CSCD 北大核心 2016年第11期3499-3502,共4页 Application Research of Computers
基金 国家科技支撑计划资助项目(2011BAK05B04) 上海市科委资助项目(14DZ2260800)
关键词 手写汉字检测 无分割 SIFT 动态时间规整 Chinese handwritten word spotting segmentation-free SIFT dynamic time warping
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  • 1Manmatha R, Han C, Riseman E M. Word spotting: a new approach to indexing handwriting[ C]//Proc of IEEE Conference on Computer Vision and Pattern Recognition. 1996: 631-637.
  • 2Rath T M, Manmatha R. Features for word spotting in historical manu- scripts[ C l// Proc of IEEE Conference on Document Analysis and Recognition. 2003 : 218-222.
  • 3Zhang Xiafen, Zhuang Yueting. Dynamic time warping for Chinese calligraphic character matching and recognizing[ J]. Pattern Recog- nition Letters, 2012, 33(16): 2262-2269.
  • 4Fischer A, Keller A, Frinken V, et al. HMM-based word spotting in handwritten documents using subword models[ C ]//Proc of the 20th International Conference on Pattern Recognition. Washington DC: IEEE Computer Society,2010 : 3416-3419.
  • 5Frinken V, Fischer A, Manmatha R, et al. A novel word spotting method based on recurrent neural networks[ J]. IEEE Trans on Pat- tern Analysis and Machine Intelligence, 2012, 34 (2) : 211-224.
  • 6Huang Liang, Yin Fei, Chen Qinghu, et al. Keyword spotting in un- constrained handwritten Chinese documents using contextual word model[J]. Image and Vision Computing, 2013, 31 (12): 958- 968.
  • 7Rusinol M, Aldavert D, Toledo R, et al. Browsing heterogeneous document collections by a segmentation- free word spotting method [ C ]//Proc of IEEE Conference on Document Analysis and Recogni- tion. 2011 : 63-67.
  • 8Rothacker L, Rusinol M, Fink G A. Bag-of-features HMMs for seg- mentation- free word spotting in handwritten documents [ C ]//Proc of IEEE Conference on Document Analysis and Recognition. 2013: 1305-1309.
  • 9Zhang Xi, Tan C L. Handwritten word image matching based on heat kernel signature [ C ]// Computer Analysis of Images and Patterns. Berlin: Springer, 2013: 42-49.
  • 10Zhang Xi, Tan C L. Segmentation-free keyword spotting for handwrit- ten documents based on heat kernel signature [ C ]// Proc of IEEE Conference on Document Analysis and Recognition. 2013 : 827-831.

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