Solving Chinese Character Puzzles Based on Character Strokes

Da Ren, Yi Cai, Weizhao Li, Ruihang Xia, Zilu Li, Qing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Chinese character puzzles are popular games in China. To solve a character puzzle, people need to fully consider the meaning and the strokes of each character in puzzles. Therefore, Chinese character puzzles are complicated and it can be a challenging task in natural language processing. In this paper, we collect a Chinese character puzzles dataset (CCPD) and design a Stroke Sensitive Character Guessing (SSCG) Model. SSCG can consider the meaning and strokes of each character. In this way, SSCG can solve Chinese character puzzles more accurately. To the best of our knowledge, it is the first work which tries to handle the Chinese character puzzles. We evaluate SSCG on CCPD. The experiment results show the effectiveness of the SSCG.

Original languageEnglish
Title of host publicationNatural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings
EditorsJie Tang, Min-Yen Kan, Dongyan Zhao, Sujian Li, Hongying Zan
Number of pages11
ISBN (Print)9783030322328
Publication statusPublished - 2019
Event8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019 - Dunhuang, China
Duration: 9 Oct 201914 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11838 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019


  • Character puzzles
  • Character strokes
  • Chinese

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Solving Chinese Character Puzzles Based on Character Strokes'. Together they form a unique fingerprint.

Cite this