A background-thinning-based approach for separating and recognizing connected handwritten digit strings

Lu Zhongkang, Zheru Chi, Siu Wan-Chi, Shi Pengfei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

55 Citations (Scopus)


Most algorithms for segmenting connected handwritten digit strings are based on the analysis of the foreground pixel distributions and the features on the upper/lower contours of the image. In this paper, a new approach is presented to segment connected handwritten two-digit strings based on the thinning of background regions. The algorithm first locates several feature points on the background skeleton of a digit image. Possible segmentation paths are then constructed by matching these feature points. With geometric property measures, all the possible segmentation paths are ranked using fuzzy rules generated from a decision-tree approach. Finally, the top ranked segmentation paths are tested one by one by an optimized nearest neighbor classifier until one of these candidates is accepted based on an acceptance criterion. Experimental results on NIST special database 3 show that our approach can achieve a correct classification rate of 92.5% with only 4.7% of digit strings rejected, which compares favorably with the other techniques tested.
Original languageEnglish
Pages (from-to)921-933
Number of pages13
JournalPattern Recognition
Issue number6
Publication statusPublished - 1 Jun 1999


  • Character recognition
  • Character segmentation
  • Connected digit strings
  • Fuzzy rules
  • Thinning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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