Unsupervised Evaluation Methods Based on Local Gray-Intensity Variances for Binarization of Historical Documents

Ramírez-Ortegón, Marte A.; Rojas, Raúl

doi:10.1109/icpr.2010.500

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…Ramirez‐Ortegon and Rojas [25] studied the efficacy of six evaluation measures of the binarisation quality for tuning binarisation methods in order to maximise the accuracy of optical character recognition (OCR) applications. The compared measures are Unsupervised Empirical Measures based on grey‐intensity variance.…”

Section: Related Workmentioning

confidence: 99%

Tool for automatic tuning of binarisation techniques

Kefali

Sari

2018

IET Image Processing

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Section: Related Workmentioning

confidence: 99%

Tool for automatic tuning of binarisation techniques

Kefali

Sari

2018

IET Image Processing

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“…Feature extraction with mean threshold for binarization has been discussed in [19,20] and with bit plane slicing in [21]. The problem of uneven illumination in images was efficiently addressed by local threshold techniques [9,[22][23][24][25][26]. The literatures have used measures of dispersion like standard deviation and variance to calculate the threshold.…”

Section: Related Workmentioning

confidence: 99%

Retrieval Architecture with Classified Query for Content Based Image Recognition

Das

Thepade²,

Bhattacharya

et al. 2016

Applied Computational Intelligence and Soft Computing

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The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology of feature extraction by image binarization technique for enhancing identification and retrieval of information using content based image recognition. The proposed algorithm was tested on two public datasets, namely, Wang dataset and Oliva and Torralba (OT-Scene) dataset with 3688 images on the whole. It has outclassed the state-of-the-art techniques in performance measure and has shown statistical significance.

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“…Hence, the spread of data is not well captured in a binarization process for feature extraction. This concern has been well handled by commencing the computation for the standard deviation and variance of gray values for local-threshold selection techniques [18]- [20]. Threshold calculation has been carried out exhaustively in the case of global-threshold selection techniques for feature extraction using binarization [21], [22].…”

Section: Related Workmentioning

confidence: 99%

Framework for Content-Based Image Identification with Standardized Multiview Features

Das

Thepade²,

Ghosh

2016

ETRI J

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Information identification with image data by means of low‐level visual features has evolved as a challenging research domain. Conventional text‐based mapping of image data has been gradually replaced by content‐based techniques of image identification. Feature extraction from image content plays a crucial role in facilitating content‐based detection processes. In this paper, the authors have proposed four different techniques for multiview feature extraction from images. The efficiency of extracted feature vectors for content‐based image classification and retrieval is evaluated by means of fusion‐based and data standardization–based techniques. It is observed that the latter surpasses the former. The proposed methods outclass state‐of‐the‐art techniques for content‐based image identification and show an average increase in precision of 17.71% and 22.78% for classification and retrieval, respectively. Three public datasets — Wang; Oliva and Torralba (OT‐Scene); and Corel — are used for verification purposes. The research findings are statistically validated by conducting a paired t‐test.

show abstract

Unsupervised Evaluation Methods Based on Local Gray-Intensity Variances for Binarization of Historical Documents

Cited by 16 publications

References 12 publications

Tool for automatic tuning of binarisation techniques

Tool for automatic tuning of binarisation techniques

Retrieval Architecture with Classified Query for Content Based Image Recognition

Framework for Content-Based Image Identification with Standardized Multiview Features

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