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Spotting Separator Points at Line Terminals in Compressed Document Images for Text-line Segmentation

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Amarnath R., P. Nagabhushan

Amarnath R. and P Nagabhushan. Spotting Separator Points at Line Terminals in Compressed Document Images for Text-line Segmentation. International Journal of Computer Applications 172(4):40-47, August 2017. BibTeX

	author = {Amarnath R. and P. Nagabhushan},
	title = {Spotting Separator Points at Line Terminals in Compressed Document Images for Text-line Segmentation},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {172},
	number = {4},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {40-47},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2017915133},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Line separators are used to segregate text-lines from one another in document image analysis. Finding the separator points at every line terminal in a document image would enable text-line segmentation. In particular, identifying the separators in handwritten text could be a thrilling exercise. Obviously it would be challenging to perform this in the compressed version of a document image and that is the proposed objective in this research. Such an effort would prevent the computational burden of decompressing a document for text-line segmentation. Since document images are generally compressed using run length encoding (RLE) technique as per the CCITT standards, the first column in the RLE will be a white column. The value (depth) in the white column is very low when a particular line is a text line and the depth could be larger at the point of text line separation. A longer consecutive sequence of such larger depth should indicate the gap between the text lines, which provides the separator region. In case of over separation and under separation issues, corrective actions such as deletion and insertion are suggested respectively. An extensive experimentation is conducted on the compressed images of the benchmark datasets of ICDAR13 and Alireza et al [17] to demonstrate the efficacy.


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Line separators, Document image analysis, Handwritten text, Compression and decompression, RLE, CCITT.