Title: Degraded Document Image Binarization using Structural Symmetry of Strokes
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| Authors: Jia, FX; Shi, CZ; He, K; Wang, CH; Xiao, BH
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| Author Full Names: Jia, Fuxi; Shi, Cunzhao; He, Kun; Wang, Chunheng; Xiao, Baihua
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| Source: PATTERN RECOGNITION, 74 225-240; 10.1016/j.patcog.2017.09.032 FEB 2018
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| Language: English
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| Abstract: This paper presents an effective approach for the local threshold binarization of degraded document images. We utilize the structural symmetric pixels (SSPs) to calculate the local threshold in neighborhood and the voting result of multiple thresholds will determine whether one pixel belongs to the foreground or not. The SSPs are defined as the pixels around strokes whose gradient magnitudes are large enough and orientations are symmetric opposite. The compensated gradient map is used to extract the SSP so as to weaken the influence of document degradations. To extract SSP candidates with large magnitudes and distinguish the faint characters and bleed-through background, we propose an adaptive global threshold selection algorithm. To further extract pixels with opposite orientations, an iterative stroke width estimation algorithm is applied to ensure the proper size of neighborhood used in orientation judgement. At last, we present a multiple threshold vote based framework to deal with some inaccurate detections of SSP. The experimental results on seven public document image binarization datasets show that our method is accurate and robust compared with many traditional and state-of-the-art document binarization approaches based on multiple evaluation measures. (C) 2017 Elsevier Ltd. All rights reserved.
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| ISSN: 0031-3203
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| eISSN: 1873-5142
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| IDS Number: FP3WD
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| Unique ID: WOS:000417547800018
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