DocumentCode
3046562
Title
An Improved Drop-fall Algorithm Based on Background Analysis for Handwritten Digits Segmentation
Author
Rui, Ma ; Du Jie ; Yunhua, Gu ; Yunyang, Yan
Author_Institution
Comput. & Software Inst., Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China
Volume
4
fYear
2009
fDate
19-21 May 2009
Firstpage
374
Lastpage
378
Abstract
Among several contour splitting algorithms, Drop-fall algorithm has shown good performance, but it is defective in locating the start point of the algorithm and the seeping process in vertical direction inside character stroke block. For the purpose of solving the problems produced by the defections, in this paper Drop-fall algorithm is improved by citing water reservoir concept to use the information coming from the background region of touching digits. On the basis of the topological features of background region, the range of searching start point in algorithm is dictated and the optimal position for the seeping process is determined. For connected digits sharing a long stroke segment, instead of the seeping process in vertical direction, centerline is extracted from common touching area to be used as a long tilt path which is cutting across the black block when the drop enters the junction of connected digits. Experimental results show that the improved algorithm obtains more satisfied segmentation and works better than former.
Keywords
handwritten character recognition; image segmentation; background analysis; contour splitting algorithms; drop-fall algorithm; handwritten digits segmentation; seeping process; topological features; Algorithm design and analysis; Character recognition; Information analysis; Information science; Intelligent systems; Optical character recognition software; Performance analysis; Software algorithms; Software performance; Water resources; Drop-fall Algorithm; Touching Digits; Water Reservoir;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
Conference_Location
Xiamen
Print_ISBN
978-0-7695-3571-5
Type
conf
DOI
10.1109/GCIS.2009.60
Filename
5209274
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