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2012 (Vol. 4, Issue: 24)
Article Information:

An Efficient Character Segmentation Based on VNP Algorithm

S. Chitrakala, Srivardhini Mandipati, S. Preethi Raj and Gottumukkala Asisha
Corresponding Author:  S. Chitrakala 

Key words:  Dissection based segmentation, optical character recognition, segmentation, vertical projection, , ,
Vol. 4 , (24): 5438-5442
Submitted Accepted Published
March 18, 2012 April 14, 2012 December 15, 2012

Character segmentation is an important preprocessing stage in image processing applications such as OCR, License Plate Recognition, electronic processing of checks in banks, form processing and, label and barcode recognition. It is essential to have an efficient character segmentation technique because it affects the performance of all the processes that follow and hence, the overall system accuracy. Vertical projection profile is the most common segmentation technique. However, the segmentation results are not always correct in cases where pixels of adjacent characters fall on the same scan line and a minimum threshold is not observed in the histogram to segment the respective adjacent characters. In this study, a character segmentation technique based on Visited Neighbor Pixel (VNP) Algorithm is proposed, which is an improvement to the vertical projection profile technique. VNP Algorithm performs segmentation based on the connectedness of the pixels on the scan line with that of the previously visited pixels. Therefore, a clear line of separation is found even when the threshold between two adjacent characters is not minimal. The segmentation results of the traditional vertical projection profile and the proposed method are compared with respect to a few selected fonts and the latter, with an average accuracy of approximately 94%, has shown encouraging results.
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  Cite this Reference:
S. Chitrakala, Srivardhini Mandipati, S. Preethi Raj and Gottumukkala Asisha, 2012. An Efficient Character Segmentation Based on VNP Algorithm.  Research Journal of Applied Sciences, Engineering and Technology, 4(24): 5438-5442.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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