Hardware-Based Document Image Thresholding Techniques Using DSP Builder and Simulink

2021 ◽  
pp. 207-220
Author(s):  
N. Habibunnisha ◽  
D. Nedumaran
Keyword(s):  
Document Image ◽  
Image Thresholding ◽  
Dsp Builder

scholarly journals FADIT: Fast Document Image Thresholding

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 46
Author(s):  
Yufang Min ◽  
Yaonan Zhang
Keyword(s):  
High Probability ◽  
State Of The Art ◽  
Bayesian Framework ◽  
Document Image ◽  
The State ◽  
Criterion Function ◽  
Image Thresholding ◽  
Thresholding Method ◽  
Art Methods ◽  
New Criterion

We propose a fast document image thresholding method (FADIT) and evaluations of the two classic methods for demonstrating the effectiveness of FADIT. We put forward two assumptions: (1) the probability of the occurrence of grayscale text and background is ideally two constants, and (2) a pixel with a low grayscale has a high probability of being classified as text and a pixel with a high grayscale has a high probability of being classified as background. With the two assumptions, a new criterion function is applied to document image thresholding in the Bayesian framework. The effectiveness of the method has been borne of a quantitative metric as well as qualitative comparisons with the state-of-the-art methods.

scholarly journals Optimalisasi Image Thresholding pada Optical Character Recognition Pada Sistem Digitalisasi dan Pencarian Dokumen

Petir ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 1-11
Author(s):  
Ridwan Rismanto ◽  
Arief Prasetyo ◽  
Dyah Ayu Irawati
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Threshold Value ◽  
Document Image ◽  
Great Effort ◽  
Processing Stage ◽  
Image Thresholding ◽  
Optical Character ◽  
Value Sets ◽  
Readable Text

The administration activity in an institute is largerly done by using a paper based mailing and document as a media. Therefore, a great effort needs to be performed in the case of management and archiving, in the form of providing storage space through the categorizing system. Digitalization of document by scanning it into a digital image is one of the solution to reduce the effort to perform the work of archiving and categorizing such document. It also provide searching feature in the form of metadata, that is manually written during the digitalization process. The metadata can contains the title of document, summary, or category. The needs to manually input this metadata can be solved by utilizing Optical Character Recognition (OCR) that converts any text in the document into readable text storing in the database system. This research focused on the implementation of the OCR system to extract text in the scanned document image and performing optimization of the pre-processing stage which is Image Thresholding. The aim of the optimization is to increase OCR accuracy by tuning threshold value of given value sets, and resulting 0.6 as the best thresholding value. Experiment performed by processing text extraction towards several scanned document and achieving accuration rate of 92.568%.

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

2019 ◽  
Vol 2 (3) ◽  
pp. 206-215
Author(s):  
Alesya Ishchenko ◽  
Alexandr Nesteryuk ◽  
Marina Polyakova
Keyword(s):  
Document Image ◽  
Linear Filtration

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

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.

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

2020 ◽  
Vol 64 (3) ◽  
pp. 30401-1-30401-14 ◽  
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.

Image thresholding method based on Fourier spectrum and moment-preserving principle

2010 ◽  
Vol 30 (8) ◽  
pp. 2094-2097 ◽  
Author(s):  
Xin-ming ZHANG ◽  
Shuang LI ◽  
Yan-bin ZHENG ◽  
Hui-yun ZHANG
Keyword(s):  
Fourier Spectrum ◽  
Image Thresholding ◽  
Thresholding Method

scholarly journals An enhanced binarization framework for degraded historical document images

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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