期刊名称：Facta universitatis - series: Electronics and Energetics
印刷版ISSN：0353-3670
电子版ISSN：2217-5997
出版年度：2018
卷号：31
期号：3
页码：461-485
DOI：10.2298/FUEE1803461A
出版社：University of Niš
摘要：This paper presents an efficient new image compression and decompression methods for document images, intended for usage in the pre-processing stage of an OCR system designed for needs of the “Nikola Tesla Museum” in Belgrade. Proposed image compression methods exploit the Run-Length Encoding (RLE) algorithm and an algorithm based on document character contour extraction, while an iterative scanline fill algorithm is used for image decompression. Image compression and decompression methods are compared with JBIG2 and JPEG2000 image compression standards. Segmentation accuracy results for ground-truth documents are obtained in order to evaluate the proposed methods. Results show that the proposed methods outperform JBIG2 compression regarding the time complexity, providing up to 25 times lower processing time at the expense of worse compression ratio results, as well as JPEG2000 image compression standard, providing up to 4-fold improvement in compression ratio. Finally, time complexity results show that the presented methods are sufficiently fast for a real time character segmentation system. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. III44006-10]
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