Dynamic Time Warping for Chinese calligraphic character matching and recognizing

Historical Chinese calligraphy is now being scanned by ongoing project of Universal Digital Library to enable universal access. 483 Calligraphic page images are segmented into 13,351 individual characters, followed by character feature extraction. When an image query submits, a short list of candidates from the database is selected according to simple features of stroke transect. Then the selected candidates are ranked by shape matching using two-dimensional Dynamic Time Warping (DTW). After that, similar calligraphic characters are retrieved and ranked, then the query is recognized as the label of the most similar retrieved characters.

[1]  David E Polett Empowering the Voter: A Mathematical Analysis of Borda Count Elections with Non-Linear Preferences , 2010 .

[2]  Chew Lim Tan,et al.  A model of stroke extraction from Chinese character images , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Abdelmajid Ben Hamadou,et al.  Off-line handwritten word recognition using multi-stream hidden Markov models , 2010, Pattern Recognit. Lett..

[4]  Björn W. Schuller,et al.  A multidimensional dynamic time warping algorithm for efficient multimodal fusion of asynchronous data streams , 2009, Neurocomputing.

[5]  Chafic Mokbel,et al.  Combining Slanted-Frame Classifiers for Improved HMM-Based Arabic Handwriting Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Alan F. Smeaton,et al.  Word matching using single closed contours for indexing handwritten historical documents , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[7]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[8]  Jin Hyung Kim,et al.  Digitalizing scheme of handwritten Hanja historical documents , 2004, First International Workshop on Document Image Analysis for Libraries, 2004. Proceedings..

[9]  Eamonn J. Keogh,et al.  Exact indexing of dynamic time warping , 2002, Knowledge and Information Systems.

[10]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[11]  Its'hak Dinstein,et al.  Binarization, character extraction, and writer identification of historical Hebrew calligraphy documents , 2007, International Journal of Document Analysis and Recognition (IJDAR).

[12]  Jitendra Malik,et al.  Efficient shape matching using shape contexts , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Zhang Jun-song,et al.  Denoising of Chinese calligraphy tablet images based on run-length statistics and structure characteristic of character strokes * , 2006 .

[14]  Yueting Zhuang,et al.  Skeleton-Based Recognition of Chinese Calligraphic Character Image , 2008, PCM.

[15]  R. Manmatha,et al.  Word spotting for historical documents , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[16]  Yueting Zhuang,et al.  Retrieval of Chinese Calligraphic Character Image , 2004, PCM.

[17]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[18]  Feng Lin,et al.  Off-line handwritten Chinese character stroke extraction , 2002, Object recognition supported by user interaction for service robots.

[19]  Jitendra Malik,et al.  Easily adaptable handwriting recognition in historical manuscripts , 2007 .