THE TECHNIQUE OF EXTRACTION TEXT AREAS ON SCANNED DOCUMENT IMAGE USING LINEAR FILTRATION

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

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An Adaptive Binarization Method for Cost-efficient Document Image System in Wavelet Domain

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.3.030401 ◽

2020 ◽

Vol 64 (3) ◽

pp. 30401-1-30401-14 ◽

Cited By ~ 1

Author(s):

Chih-Hsien Hsia ◽

Ting-Yu Lin ◽

Jen-Shiun Chiang

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Document Image ◽

Background Information ◽

Raspberry Pi ◽

Discrete Wavelet ◽

Image System ◽

Low Frequencies ◽

Cost Efficient ◽

Binarization Method

Abstract In recent years, the preservation of handwritten historical documents and scripts archived by digitized images has been gradually emphasized. However, the selection of different thicknesses of the paper for printing or writing is likely to make the content of the back page seep into the front page. In order to solve this, a cost-efficient document image system is proposed. In this system, the authors use Adaptive Directional Lifting-Based Discrete Wavelet Transform to transform image data from spatial domain to frequency domain and perform on high and low frequencies, respectively. For low frequencies, the authors use local threshold to remove most background information. For high frequencies, they use modified Least Mean Square training algorithm to produce a unique weighted mask and perform convolution on original frequency, respectively. Afterward, Inverse Adaptive Directional Lifting-Based Discrete Wavelet Transform is performed to reconstruct the four subband images to a resulting image with original size. Finally, a global binarization method, Otsu’s method, is applied to transform a gray scale image to a binary image as the output result. The results show that the difference in operation time of this work between a personal computer (PC) and Raspberry Pi is little. Therefore, the proposed cost-efficient document image system which performed on Raspberry Pi embedded platform has the same performance and obtains the same results as those performed on a PC.

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Secure exact authentication in binary document image watermarking

IET International Conference on Visual Information Engineering (VIE 2006) ◽

10.1049/cp:20060497 ◽

2006 ◽

Cited By ~ 2

Author(s):

N.B. Puhan ◽

A.T.S. Ho

Keyword(s):

Image Watermarking ◽

Document Image

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An enhanced binarization framework for degraded historical document images

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-021-00556-4 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Wei Xiong ◽

Lei Zhou ◽

Ling Yue ◽

Lirong Li ◽

Song Wang

Keyword(s):

Document Image ◽

Morphological Operations ◽

Document Images ◽

Minimum Entropy ◽

Stroke Width ◽

Background Estimation ◽

Structuring Element ◽

Document Image Binarization ◽

Benchmark Datasets ◽

Stroke Width Transform

AbstractBinarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

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Document Image Classification: Progress over Two Decades

Neurocomputing ◽

10.1016/j.neucom.2021.04.114 ◽

2021 ◽

Author(s):

Li Liu ◽

Zhiyu Wang ◽

Taorong Qiu ◽

Qiu Chen ◽

Yue Lu ◽

...

Keyword(s):

Image Classification ◽

Document Image

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Unsupervised Neural Domain Adaptation for Document Image Binarization

Pattern Recognition ◽

10.1016/j.patcog.2021.108099 ◽

2021 ◽

pp. 108099

Author(s):

Francisco J. Castellanos ◽

Antonio-Javier Gallego ◽

Jorge Calvo-Zaragoza

Keyword(s):

Domain Adaptation ◽

Document Image ◽

Image Binarization ◽

Document Image Binarization

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Arabic Cursive Text Recognition from Natural Scene Images

Applied Sciences ◽

10.3390/app9020236 ◽

2019 ◽

Vol 9 (2) ◽

pp. 236 ◽

Cited By ~ 6

Author(s):

Saad Ahmed ◽

Saeeda Naz ◽

Muhammad Razzak ◽

Rubiyah Yusof

Keyword(s):

Recognition System ◽

Document Image ◽

Text Recognition ◽

Chinese Script ◽

Challenging Problem ◽

Future Directions ◽

Scene Text ◽

Comprehensive Survey ◽

Recognition Systems ◽

Scene Text Recognition

This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers.

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