Script Identification of Camera Based Bilingual Document Images Using SFTA Features

2022 ◽  
pp. 811-822
Author(s):  
B.V. Dhandra ◽  
Satishkumar Mallappa ◽  
Gururaj Mukarambi
Keyword(s):  
Block Size ◽  
Texture Features ◽  
Input Image ◽  
Document Image ◽  
Optimal Size ◽  
Document Images ◽  
Binary Images ◽  
Script Identification ◽  
Unified Algorithm ◽  
Block Sizes

In this article, the exhaustive experiment is carried out to test the performance of the Segmentation based Fractal Texture Analysis (SFTA) features with nt = 4 pairs, and nt = 8 pairs, geometric features and their combinations. A unified algorithm is designed to identify the scripts of the camera captured bi-lingual document image containing International language English with each one of Hindi, Kannada, Telugu, Malayalam, Bengali, Oriya, Punjabi, and Urdu scripts. The SFTA algorithm decomposes the input image into a set of binary images from which the fractal dimension of the resulting regions are computed in order to describe the segmented texture patterns. This motivates use of the SFTA features as the texture features to identify the scripts of the camera-based document image, which has an effect of non-homogeneous illumination (Resolution). An experiment is carried on eleven scripts each with 1000 sample images of block sizes 128 × 128, 256 × 256, 512 × 512 and 1024 × 1024. It is observed that the block size 512 × 512 gives the maximum accuracy of 86.45% for Gujarathi and English script combination and is the optimal size. The novelty of this article is that unified algorithm is developed for the script identification of bilingual document images.

Script Identification of Camera Based Bilingual Document Images Using SFTA Features

2019 ◽  
Vol 15 (4) ◽  
pp. 1-12
Author(s):  
B.V. Dhandra ◽  
Satishkumar Mallappa ◽  
Gururaj Mukarambi
Keyword(s):  
Block Size ◽  
Texture Features ◽  
Input Image ◽  
Document Image ◽  
Optimal Size ◽  
Document Images ◽  
Binary Images ◽  
Script Identification ◽  
Unified Algorithm ◽  
Block Sizes

In this article, the exhaustive experiment is carried out to test the performance of the Segmentation based Fractal Texture Analysis (SFTA) features with nt = 4 pairs, and nt = 8 pairs, geometric features and their combinations. A unified algorithm is designed to identify the scripts of the camera captured bi-lingual document image containing International language English with each one of Hindi, Kannada, Telugu, Malayalam, Bengali, Oriya, Punjabi, and Urdu scripts. The SFTA algorithm decomposes the input image into a set of binary images from which the fractal dimension of the resulting regions are computed in order to describe the segmented texture patterns. This motivates use of the SFTA features as the texture features to identify the scripts of the camera-based document image, which has an effect of non-homogeneous illumination (Resolution). An experiment is carried on eleven scripts each with 1000 sample images of block sizes 128 × 128, 256 × 256, 512 × 512 and 1024 × 1024. It is observed that the block size 512 × 512 gives the maximum accuracy of 86.45% for Gujarathi and English script combination and is the optimal size. The novelty of this article is that unified algorithm is developed for the script identification of bilingual document images.

scholarly journals Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features

2019 ◽  
Vol 8 (2) ◽  
pp. 2988-2993
Keyword(s):  
English Language ◽  
Feature Vector ◽  
Video Camera ◽  
Input Image ◽  
Document Image ◽  
Gray Level ◽  
Indian Languages ◽  
Binary Images ◽  
Script Identification ◽  
Text Document

Most of the documents in various application areas like Government, Business and Research are available in the form of bi-lingual/multi-lingual text document. The multilingual documents are captured from video/camera for identification of script of the text document for automatic reading and editing. In this paper, an attempt is made to address the problem of script identification from camera captured document images using SFTA features. The input image is decomposed into a group of binary images by applying TTBD with fixing the number of the threshold as t n =3 empirically, on each decomposed binary image, Box Count, Mean Gray Level, and Pixel Count are extracted to form the feature vector. This feature vector is submitted to K-NN classifier to identify the scripts of the input document image. In all 10 scripts of the Indian languages are considered along with common English language as bi-lingual documents. The novelty of the paper is that 7 features are selected as potential features to obtain the highest accuracy. Features like Box Count (3), Mean Gray Level (2), and Pixel Count (2) have obtained the 87.02% recognition accuracy for English and Hindi Script combinations for the collected dataset and encouraging results for other combinations. These 7 potential features were selected using the technique named as feed-forward feature selection, from the set all 18 features.

scholarly journals Hybrid Method for Elimination of Uneven Illumination from Camera-based Document Images

2020 ◽  
Vol 9 (3) ◽  
pp. 3566-3570
Keyword(s):  
Comparative Analysis ◽  
Input Image ◽  
Document Image ◽  
Document Images ◽  
Uniform Size ◽  
Enhancement Technique ◽  
Split And Merge ◽  
Noise Amplification ◽  
Document Binarization ◽  
Binarized Image

In this paper, is addressed the issues of uneven lighting and complex background of the camera-based document image using hybridization of new split and merge method. The input image is divided into four uniform size sub-images, and CLAHE enhancement technique was applied to all four sub-images to rectify noise amplification in each part of input image and then adaptive document binarization operation was used on four sub-images. Then, all four sub section images are merged to get the binarized image with elimination of noises, uneven illumination. Encouraging results are obtained by this method as compared on to other existing methods of Sauvola, Feng and Kasar found in the literature. The comparative analysis is made on collected datasets, as the standard datasets are not available.

A Unified Algorithm for Identification of Various Tabular Structures from Document Images

2012 ◽  
pp. 1-28
Author(s):  
Sekhar Mandal ◽  
Amit K. Das ◽  
Partha Bhowmick ◽  
Bhabatosh Chanda
Keyword(s):  
Image Segmentation ◽  
Document Image ◽  
Experimental Results ◽  
Document Images ◽  
Segmentation Process ◽  
Considerable Simplification ◽  
Unified Algorithm ◽  
Distinguishing Features ◽  
Document Page ◽  
Document Image Segmentation

This paper presents a unified algorithm for segmentation and identification of various tabular structures from document page images. Such tabular structures include conventional tables and displayed math-zones, as well as Table of Contents (TOC) and Index pages. After analyzing the page composition, the algorithm initially classifies the input set of document pages into tabular and non-tabular pages. A tabular page contains at least one of the tabular structures, whereas a non-tabular page does not contain any. The approach is unified in the sense that it is able to identify all tabular structures from a tabular page, which leads to a considerable simplification of document image segmentation in a novel manner. Such unification also results in speeding up the segmentation process, because the existing methodologies produce time-consuming solutions for treating different tabular structures as separate physical entities. Distinguishing features of different kinds of tabular structures have been used in stages in order to ensure the simplicity and efficiency of the algorithm and demonstrated by exhaustive experimental results.

scholarly journals Script Identification from Printed Indian Document Images and Performance Evaluation Using Different Classifiers

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Sk Md Obaidullah ◽  
Anamika Mondal ◽  
Nibaran Das ◽  
Kaushik Roy
Keyword(s):  
Model Building ◽  
Real Life ◽  
Document Image ◽  
Document Images ◽  
Accuracy Rate ◽  
Script Identification ◽  
Average Accuracy ◽  
Analysis Experiment ◽  
And Performance ◽  
Simple Logistic

Identification of script from document images is an active area of research under document image processing for a multilingual/ multiscript country like India. In this paper the real life problem of printed script identification from official Indian document images is considered and performances of different well-known classifiers are evaluated. Two important evaluating parameters, namely, AAR (average accuracy rate) and MBT (model building time), are computed for this performance analysis. Experiment was carried out on 459 printed document images with 5-fold cross-validation. Simple Logistic model shows highest AAR of 98.9% among all. BayesNet and Random Forest model have average accuracy rate of 96.7% and 98.2% correspondingly with lowest MBT of 0.09 s.

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.

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.

scholarly journals A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 991
Author(s):  
Yuta Nakahara ◽  
Toshiyasu Matsushima
Keyword(s):  
Computational Cost ◽  
Block Size ◽  
Input Image ◽  
Generative Model ◽  
Image Size ◽  
Variable Block ◽  
General Data ◽  
The Difference ◽  
Segmentation Of Images ◽  
Target Data

In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in discussing the difference between the expected code length and the entropy of the stochastic generative model. We solve this difficulty for a class of images, in which they have non-stationarity among segments. In this paper, we propose a novel stochastic generative model of images by redefining the implicit stochastic generative model in a previous coding procedure. Our model is based on the quadtree so that it effectively represents the variable block size segmentation of images. Then, we construct the Bayes code optimal for the proposed stochastic generative model. It requires the summation of all possible quadtrees weighted by their posterior. In general, its computational cost increases exponentially for the image size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the image size without loss of optimality. As a result, the derived algorithm has a better average coding rate than that of JBIG.

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