3.8 Article Proceedings Paper

Chinese cursive character detection method

期刊

JOURNAL OF ENGINEERING-JOE
卷 2020, 期 13, 页码 626-629

出版社

WILEY
DOI: 10.1049/joe.2019.1208

关键词

feature extraction; handwritten character recognition; text analysis; text detection; history; art; natural languages; text detection; SE-seglink method; Chinese cursive character detection method; Chinese cursive script; distinctive calligraphy art; connected writing; text recognition; cursive image dataset; continuous strokes text; image feature extraction; traditional cultures; ICDAR2015 benchmark image dataset

资金

  1. National Natural Science Foundation of China [61562008, 61772091, 61802035]
  2. National Natural Science Foundation of Guangxi [2016GXNSFAA380209]
  3. Natural Science Foundation of Guangxi [2018GXNSFDA138005]
  4. Project of Science Research and Technology Development in Guangxi [AA18118047, AD18126015, AB16380272, 20175177]
  5. Sichuan Science and Technology Program [2018JY0448, 2019YFG0106, 2019YFS0067]
  6. Innovative Research Team Construction Plan in Universities of Sichuan Province [18TD0027]
  7. Soft Science Foundation of Chengdu [2017-RK00-00053-ZF]
  8. Scientific Research Foundation for Young Academic Leaders of Chengdu University of Information Technology [J201701]
  9. Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology [KYTZ201715, KYTZ201750]
  10. Guangdong Key Laboratory Project [2017B030314073]
  11. BAGUI Scholar Program of Guangxi Zhuang Autonomous Region of China

向作者/读者索取更多资源

Chinese is a widely used language in the world. Chinese cursive script is one of the most distinctive calligraphy art and traditional cultures of China. However, for its connected writing, there is a lack of research on text recognition for cursive images. Here, the authors construct a small cursive image dataset named as Chinese Cursive and there are 523 images in this dataset. It contains continuous strokes text, recognises difficulty etc. Each cursive character is corresponded to a label. The authors proposed a cursive detection method named as SE-seglink for the dataset. The SE-seglink further enhances the image feature extraction. Compared to the existing methods, the SE-seglink performs better in recognising cursive scripts and improves the precision of text detection in cursive images. After multiple sets of comparative experiments, the effectiveness of the SE-seglink method was evaluated by the experiment on the benchmark image dataset ICDAR2015.

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