DocumentCode
2425964
Title
Color document image segmentation for automated document entry systems
Author
Suen, Hong-Ming ; Wang, Jhing-Fa
Author_Institution
Inst. of Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
Volume
1
fYear
1996
fDate
26-29 Nov 1996
Firstpage
131
Abstract
Monochrome document image segmentation has been studied for over ten years. On the other hand, how to segment color document images is still an open research field. We propose an approach for segmenting color document images. Unlike the common practice in monochrome documents that objects are black on a white background the components in color documents can be any color. To cope with their variety, the first step of our approach is to create a binary image of edge-representation. Then page segmentation is carried out in the binary image using the CRLA procedure. Finally, we utilize the geometric features and the color information to classify the segmented blocks into text lines and picture components. The identified text lines are then further transformed into the white-background/black-text format for OCR processing. The proposed approach was implemented on a Pentium/l33 PC and the experimental results have demonstrated its feasibility
Keywords
document image processing; edge detection; feature extraction; image colour analysis; image representation; image segmentation; microcomputer applications; CRLA procedure; OCR processing; Pentium/l33 PC; automated document entry systems; binary image; color document image segmentation; color information; edge representation; experimental results; geometric features; image edge representation; page segmentation; picture components; segmented blocks classification; text lines; white background/black text format; Color; Flowcharts; Histograms; Humans; Image edge detection; Image segmentation; Optical character recognition software; Pixel;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON '96. Proceedings., 1996 IEEE TENCON. Digital Signal Processing Applications
Conference_Location
Perth, WA
Print_ISBN
0-7803-3679-8
Type
conf
DOI
10.1109/TENCON.1996.608732
Filename
608732
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