Influence of Color-to-Gray Conversion on the Performance of Document Image Binarization: Toward a Novel Optimization Problem

Hedjam, Rachid; Nafchi, Hossein Ziaei; Kalácska, Margaret; Cheriet, Mohamed

doi:10.1109/tip.2015.2442923

Cited by 24 publications

(11 citation statements)

References 34 publications

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“…4(e). (ii) apply sobel edge detector [16], [17] for detecting the appropriate edges as shown in Fig. 4(f).…”

Section: Locate Rectangles Of Plate Vehiclementioning

confidence: 99%

“…However, the introduced methodology has been succeeding in detecting each letter and number. Table ( 16), illustrated when we reduce brightness of image (darken image), we have noticed that traditional method could not detect all of letters and numbers for Egyptian (LPs) and English (LPs). However, the introduced methodology has been succeeding in detecting each letter and number.…”

Section: Dc(f Q)mentioning

confidence: 99%

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Toward an Optimized Neutrosophic k-Means With Genetic Algorithm for Automatic Vehicle License Plate Recognition (ONKM-AVLPR)

et al. 2020

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The present paper proposes a new methodology for license plate (LP) recognition in the state of the art of image processing algorithms and an optimized neutrosophic set (NS) based on genetic algorithm (GA). First of all, we have performed some image processing techniques such as edge detection and morphological operations in order to utilize the (LP) localization. In addition, we have extracted the most salient features by implementing a new methodology using (GA) for optimizing the (NS) operations. The use of (NS) decreases the indeterminacy on the (LP) images. Moreover, k-means clustering algorithm has been applied to segment the (LP) characters. Finally, we have applied connected components labeling analysis (CCLA) algorithm for identifying the connected pixel regions and grouping the appropriate pixels into components to extract each character effectively. Several performance indices have been calculated in order to measure the system efficiency such as accuracy, sensitivity, specificity, dice, and jaccard coefficients. Moreover, we have created a database for all detected and recognized (LP) for testing purposes. Experimental results show that the proposed methodology has the ability to be suitable for both (Arabic-Egyptian) and English (LP). The proposed system achieves high degree of recognition accuracy for the whole system according to the following case studies; (i) for a high resolution Egyptian (LP), the proposed system achieves about 96.67% accuracy of correct recognition, (ii) for a low resolution-corrupted English (LP), the proposed system achieves about 94.27% accuracy. In addition, we have applied the proposed system on some sort of image disturbance i.e. (flash in image, external noise, and illumination variation), the proposed system achieves about 92.5% accuracy of correct identification. However, traditional methods achieve about 79% accuracy of correct identification in the presence of such image degradations. This reflects how the proposed system is generalized, optimized, and proposes high degree of recognition accuracy.

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“…4(e). (ii) apply sobel edge detector [16], [17] for detecting the appropriate edges as shown in Fig. 4(f).…”

Section: Locate Rectangles Of Plate Vehiclementioning

confidence: 99%

Section: Dc(f Q)mentioning

confidence: 99%

Toward an Optimized Neutrosophic k-Means With Genetic Algorithm for Automatic Vehicle License Plate Recognition (ONKM-AVLPR)

et al. 2020

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“…This problem is compounded when there are multiple foreground ink colors. For example, Hedjam et al [48] report improvements of about 5-15% across 11 binarization methods when using their proposed grayscale conversion method on images with multiple and/or non-standard foreground ink colors.…”

Section: Grayscale Conversionmentioning

confidence: 99%

Historical Document Image Binarization: A Review

Tensmeyer

Martinez

2020

SN COMPUT. SCI.

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This review provides a comprehensive view of the field of historical document image binarization with a focus on the contributions made in the last decade. After the introduction of a standard benchmark dataset with the 2009 Document Image Binarization Contest, research in the field accelerated. Besides the standard methods for image thresholding, preprocessing, and post-processing, we review the literature on methods such as statistical models, pixel classification with learning algorithms, and parameter tuning. In addition to reviewing binarization algorithms, we discuss the available public datasets and evaluation metrics, including those that require pixel-level ground truth and those that do not. We conclude with recommendations for future work.

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“…Wan and Xie [7] proposed an entropy‐based optimisation framework to increase the visual contrast of the grey‐scale image. Hedjam et al [8] learned a linear filter and proposed a preprocessing method of colour‐to‐grey conversion. Du et al [9] proposed a colour‐to‐grey conversion based on a region‐based saliency model.…”

Section: Related Workmentioning

confidence: 99%