Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition

Pangestu, Peter; Gunawan, Dennis; Hansun, Seng

doi:10.14716/ijtech.v8i5.877

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Table 1 shows the data set that has been built for the experimental purpose in Pangestu et al. [2] research and some others that had been added later after the publication of the research paper.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Images with hidden information data set for information retrieval usage

2019

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The main task in Optical Character Recognition (OCR) is to get and convert all the text characters on an image as a plain text data. However, if the image has low contrast and low exposure, an issue may occur. The characters may be hidden and can't be recovered completely. One solution that has been done and reported in 2017 is by applying histogram equalization as a pre-processing step in OCR. Here, we deliver a total of 30 sample data, some of which had been used on the research's experiment reported in 2017, and some others were added later.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Images with hidden information data set for information retrieval usage

2019

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“…As a final enhancement step, Otsu algorithm is used to obtain binary images. To treat image with low contrast and low exposure, in Pangestu et al (2017) is used histogram equalization to enhance the image contrast so the hidden information can be shown.…”

Section: Related Workmentioning

confidence: 99%

Industrial Optical Character Recognition System in Printing Quality Control of Hot-Rolled Coils Identification

Caldeira

Ciarelli

Neto

2019

J Control Autom Electr Syst

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This work presents a system designed to detect printing errors and misidentifications on steel coils that could lead to tracking problems and even guide to the delivery of the wrong product to the final client. An optical character recognition system is proposed to extract the printed identification of steel coils from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters originally printed in an arch form, ending with a classification routine based on a convolutional neural network. The proposed system presents an approach to treat lighting variations in images, covering low contrast, darker and brighter images. Experiment carried out on a data set with approximately 20,000 images achieved an accuracy higher than 98%, supporting the validity of the proposed method.

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“…Image processing technology has undergone significant growth. Therefore, to save up existing storage devices and transmission channels, methods which are able to compress data are widely investigated [1,2]. A true-color image consists of 24-bit of storage, where 8 bits for red, 8 for green, and 8 for blue [3].…”

Section: Introductionmentioning

confidence: 99%

Quality and size assessment of quantized images using K-Means++ clustering

Ongkadinata¹,

Putri²

2020

Bulletin EEI

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In this paper, an amended K-Means algorithm called K-Means++ is implemented for color quantization. K-Means++ is an improvement to the K-Means algorithm in order to surmount the random selection of the initial centroids. The main advantage of K-Means++ is the centroids chosen are distributed over the data such that it reduces the sum of squared errors (SSE). K-Means++ algorithm is used to analyze the color distribution of an image and create the color palette for transforming to a better quantized image compared to the standard K-Means algorithm. The tests were conducted on several popular true color images with different numbers of K value: 32, 64, 128, and 256. The results show that K-Means++ clustering algorithm yields higher PSNR values and lower file size compared to K-Means algorithm; 2.58% and 1.05%. It is envisaged that this clustering algorithm will benefit in many applications such as document clustering, market segmentation, image compression and image segmentation because it produces accurate and stable results.

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Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition

Cited by 8 publications

References 8 publications

Images with hidden information data set for information retrieval usage

Images with hidden information data set for information retrieval usage

Industrial Optical Character Recognition System in Printing Quality Control of Hot-Rolled Coils Identification

Quality and size assessment of quantized images using K-Means++ clustering

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