Graphics Recognition. New Trends and Challenges

Sketch maps are an intuitive way to display and communicate geographic data and an automatic processing is of great benefit for human-computer interaction. This paper presents a method for segmentation of sketch map objects as part of the sketch map understanding process. We use region-based segmentation that is robust to gaps in the drawing and can even handle open-ended streets. To evaluate this approach, we manually generated a ground truth for 20 maps and conducted a preliminary quantitative performance study.

[1]  K. Yamada,et al.  On-line Japanese character recognition experiments by an off-line method based on normalization-cooperated feature extraction , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[2]  Su Yang.  Symbol Recognition via Statistical Integration of Pixel-Level Constraint Histograms: A New Descriptor , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Online Handwritten Japanese Character String Recognition Using Conditional Random Fields , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[4]  Xiaoqing Ding,et al.  Multi-Font Printed Tibetan OCR , 2007 .

[5]  Wu Jian.  De-noising Approach for Online Handwriting Character Recognition Based on Mathematical Morphology , 2009 .

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

[7]  Wang Wei.  Study on Similitude Characters in Tibetan Character Recognition , 2002 .

[8]  Liu Fang.  Study on printed Tibetan character recognition , 2009 .

[9]  Bart Lamiroy,et al.  Pattern Recognition Methods for Querying and Browsing Technical Documentation , 2008, CIARP.

[10]  Kun Zhang,et al.  Symbol Recognition with Kernel Density Matching , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Masaki Nakagawa,et al.  'Online recognition of Chinese characters: the state-of-the-art , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Zhen-Long Bai,et al.  A Study of Nonlinear Shape Normalization for Online Handwritten Chinese Character Recognition: Dot Density vs. Line Density Equalization , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[13]  Fumitaka Kimura,et al.  Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Cheng-Lin Liu,et al.  High accuracy handwritten Chinese character recognition using LDA-based compound distances , 2008, Pattern Recognit..

[15]  Qian Jian-jun.  Application of Hidden Markov Model in On-line Recognition of Handwritten Tibetan Characters , 2009 .

[16]  Ernest Valveny,et al.  A general framework for the evaluation of symbol recognition methods , 2007, International Journal of Document Analysis and Recognition (IJDAR).

[17]  Ching Y. Suen,et al.  A fast parallel algorithm for thinning digital patterns , 1984, CACM.

[18]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[19]  Jian Wu,et al.  MRG-OHTC Database for Online Handwritten Tibetan Character Recognition , 2011, 2011 International Conference on Document Analysis and Recognition.

[20]  Salvatore Tabbone,et al.  Musings on Symbol Recognition , 2005, GREC.

[21]  Ka-Chung Leung,et al.  Recognition of handwritten Chinese characters by critical region analysis , 2010, Pattern Recognit..

[22]  Masaki Nakagawa,et al.  Error Reduction by Confusing Characters Discrimination for Online Handwritten Japanese Character Recognition , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.