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.

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.

scholarly journals An Unsupervised Classification Technique for Detection of Flipped Orientations in Document Images

2016 ◽  
Vol 6 (5) ◽  
pp. 2140
Author(s):  
Vijayashree CS ◽  
Shobha Rani ◽  
Vasudev T
Keyword(s):  
Unsupervised Learning ◽  
Unsupervised Classification ◽  
Document Image ◽  
Document Images ◽  
Orientation Direction ◽  
Average Accuracy ◽  
Direction Detection ◽  
Automatic Reading ◽  
Reading System ◽  
Specific Orientation

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Detection of text orientation in document images is of preliminary concern prior to processing of documents by Optical Character Reader. The text direction in document images should exist generally in a specific orientation, i.e.,   text direction for any automated document reading system. The flipped text orientation leads to an unambiguous result in such fully automated systems. In this paper, we focus on development of text orientation direction detection module which can be incorporated as the perquisite process in automatic reading system. Orientation direction detection of text is performed through employing directional gradient features of document image and adapts an unsupervised learning approach for detection of flipped text orientation at which the document has been originally fed into scanning device. The unsupervised learning is built on the directional gradient features of text of document based on four possible different orientations. The algorithm is experimented on document samples of printed plain English text as well as filled in pre-printed forms of Telugu script. The outcome attained by algorithm proves to be consistent and adequate with an average accuracy around 94%.</p></td></tr></tbody></table>

A ROBUST SYSTEM FOR THRESHOLDING AND SKEW DETECTION IN MIXED TEXT/GRAPHICS DOCUMENTS

2005 ◽  
Vol 05 (02) ◽  
pp. 247-265 ◽  
Author(s):  
ADNAN AMIN ◽  
SUE WU
Keyword(s):  
Automatic System ◽  
Real Life ◽  
Detection Algorithm ◽  
Document Image ◽  
Superior Performance ◽  
Document Images ◽  
Skew Detection ◽  
Robust System ◽  
Graphic Images ◽  
Scanned Images

This article presents an automatic system that takes in grayscale scanned images, which could be mixed text/graphic documents, and performs thresholding and skew detection on the document images. The system consists of two major components; multistage thresholding and skew detection. The proposed skew detection algorithm has no restriction on detectable angle range and does not rely on large blocks of text. It works well on textual document images, graphical images and mixed text and graphic images. The performance of the systems was evaluated using over 60 images that consist of real life documents like envelopes and artificial mixed text/graphic icons. The superior performance of thresholding is clear compared to other techniques from the evaluation. The skew detection algorithm is robust when compared with other methods when very few text lines are present in the document image.

Word-Level Multi-Script Indic Document Image Dataset and Baseline Results on Script Identification

2017 ◽  
Vol 7 (2) ◽  
pp. 81-94 ◽  
Author(s):  
S. K. Obaidullah ◽  
K. C. Santosh ◽  
Chayan Halder ◽  
Nibaran Das ◽  
Kaushik Roy
Keyword(s):  
State Of The Art ◽  
Document Analysis ◽  
Document Image ◽  
Script Identification ◽  
Word Level ◽  
Linear Classifier ◽  
Image Dataset ◽  
Fair Comparison ◽  
Public Datasets ◽  
Simple Logistic

Document analysis research starves from the availability of public datasets. Without publicly available dataset, one cannot make fair comparison with the state-of-the-art methods. To bridge this gap, in this paper, the authors propose a word-level document image dataset of 13 different Indic languages from 11 official scripts. It is composed of 39K words that are equally distributed i.e., 3K words per language. For a baseline results, five different classifiers: multilayer perceptron (MLP), fuzzy unordered rule induction algorithm (FURIA), simple logistic (SL), library for linear classifier (LibLINEAR) and bayesian network (BayesNet) classifiers are used with three state-of-the-art features: spatial energy (SE), wavelet energy (WE) and the Radon transform (RT), including their possible combinations. The authors observed that MLP provides better results when all features are used, and achieved the bi-script accuracy of 99.24% (keeping Roman common), 98.38% (keeping Devanagari common) and tri-script accuracy of 98.19% (keeping both Devanagari and Roman common).

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.

An Unsupervised Classification Technique for Detection of Flipped Orientations in Document Images

2016 ◽  
Vol 6 (5) ◽  
pp. 2140
Author(s):  
Vijayashree CS ◽  
Shobha Rani ◽  
Vasudev T
Keyword(s):  
Unsupervised Learning ◽  
Unsupervised Classification ◽  
Document Image ◽  
Document Images ◽  
Orientation Direction ◽  
Average Accuracy ◽  
Direction Detection ◽  
Automatic Reading ◽  
Reading System ◽  
Specific Orientation

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Detection of text orientation in document images is of preliminary concern prior to processing of documents by Optical Character Reader. The text direction in document images should exist generally in a specific orientation, i.e.,   text direction for any automated document reading system. The flipped text orientation leads to an unambiguous result in such fully automated systems. In this paper, we focus on development of text orientation direction detection module which can be incorporated as the perquisite process in automatic reading system. Orientation direction detection of text is performed through employing directional gradient features of document image and adapts an unsupervised learning approach for detection of flipped text orientation at which the document has been originally fed into scanning device. The unsupervised learning is built on the directional gradient features of text of document based on four possible different orientations. The algorithm is experimented on document samples of printed plain English text as well as filled in pre-printed forms of Telugu script. The outcome attained by algorithm proves to be consistent and adequate with an average accuracy around 94%.</p></td></tr></tbody></table>

Animal Recognition and Eye Movements

2010 ◽  
Vol 57 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Toby J. Lloyd-Jones ◽  
Juergen Gehrke ◽  
Jason Lauder
Keyword(s):  
Eye Movements ◽  
Response Times ◽  
Real Life ◽  
Object Orientation ◽  
Decision Task ◽  
Line Drawings ◽  
Object Decision ◽  
Individual Features ◽  
And Performance

We assessed the importance of outline contour and individual features in mediating the recognition of animals by examining response times and eye movements in an animal-object decision task (i.e., deciding whether or not an object was an animal that may be encountered in real life). There were shorter latencies for animals as compared with nonanimals and performance was similar for shaded line drawings and silhouettes, suggesting that important information for recognition lies in the outline contour. The most salient information in the outline contour was around the head, followed by the lower torso and leg regions. We also observed effects of object orientation and argue that the usefulness of the head and lower torso/leg regions is consistent with a role for the object axis in recognition.

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 Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3776
Author(s):  
Carsten Jaeschke ◽  
Marta Padilla ◽  
Johannes Glöckler ◽  
Inese Polaka ◽  
Martins Leja ◽  
...  
Keyword(s):  
Real Life ◽  
Sensor Arrays ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Calibration Transfer ◽  
Early Disease Detection ◽  
Exhaled Breath Analysis ◽  
Breath Analyzer ◽  
Analysis Experiment ◽  
Before And After

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

Sign in / Sign up

Export Citation Format

Share Document