A Dual PSO-Adaptive Mean Shift for Preprocessing Optimization on Degraded Document Images

Aicha Eutamene, Mohamed Khireddine Kholladi, Djamel Gaceb and Hacene Belhadef
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Aicha Eutamene: University of Constantine 2, Constantine, Algeria
Mohamed Khireddine Kholladi: Echahid Hamma Lakhdar University of El Oued, El Khroub, Algeria
Djamel Gaceb: M'Hamed Bougara University, Boumerdes, Algeria
Hacene Belhadef: University of Constantine 2, Constantine, Algeria

International Journal of Applied Metaheuristic Computing (IJAMC), 2017, vol. 8, issue 1, 61-76

Abstract: In the two past decades, solving complex search and optimization problems with bioinspired metaheuristic algorithms has received considerable attention among researchers. In this paper, the image preprocessing is considered as an optimization problem and the PSO (Particle Swarm Optimization) algorithm was been chosen to solve it in order to select the best parameters. The document image preprocessing step is the basis of all other steps in OCR (Optical Character Recognition) system, such as binarization, segmentation, skew correction, layout extraction, textual zones detection and OCR. Without preprocessing, the presence of degradation in the image significantly reduces the performance of these steps. The authors' contribution focuses on the preprocessing of type: smoothing and filtering document images using a new Adaptive Mean Shift algorithm based on the integral image. The local adaptation to the image quality accelerates the conventional smoothing avoiding the preprocessing of homogeneous zones. The authors' goal is to show how PSO algorithm can improve the results quality and the choice of parameters in pre-processing's methods of document images. Comparative studies as well as tests over the existing dataset have been reported to confirm the efficiency of the proposed approach.

Date: 2017
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