Word-Level Thirteen Official Indic Languages Database for Script Identification in Multi-script Documents

2017 ◽  
pp. 16-27
Author(s):  
Sk Md Obaidullah ◽  
K. C. Santosh ◽  
Chayan Halder ◽  
Nibaran Das ◽  
Kaushik Roy
Keyword(s):  
Script Identification ◽  
Word Level

Word-Level Script Identification Using Texture Based Features

2015 ◽  
Vol 4 (2) ◽  
pp. 74-94
Author(s):  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Statistical Significance ◽  
Document Image ◽  
Statistical Significance Testing ◽  
Script Identification ◽  
Word Level ◽  
Histograms Of Oriented Gradients ◽  
Handwritten Text ◽  
Identification Technique

Script identification is an appealing research interest in the field of document image analysis during the last few decades. The accurate recognition of the script is paramount to many post-processing steps such as automated document sorting, machine translation and searching of text written in a particular script in multilingual environment. For automatic processing of such documents through Optical Character Recognition (OCR) software, it is necessary to identify different script words of the documents before feeding them to the OCR of individual scripts. In this paper, a robust word-level handwritten script identification technique has been proposed using texture based features to identify the words written in any of the seven popular scripts namely, Bangla, Devanagari, Gurumukhi, Malayalam, Oriya, Telugu, and Roman. The texture based features comprise of a combination of Histograms of Oriented Gradients (HOG) and Moment invariants. The technique has been tested on 7000 handwritten text words in which each script contributes 1000 words. Based on the identification accuracies and statistical significance testing of seven well-known classifiers, Multi-Layer Perceptron (MLP) has been chosen as the final classifier which is then tested comprehensively using different folds and with different epoch sizes. The overall accuracy of the system is found to be 94.7% using 5-fold cross validation scheme, which is quite impressive considering the complexities and shape variations of the said scripts. This is an extended version of the paper described in (Singh et al., 2014).

Automatic Indic script identification from handwritten documents: page, block, line and word-level approach

2017 ◽  
Vol 10 (1) ◽  
pp. 87-106 ◽  
Author(s):  
Sk Md Obaidullah ◽  
K. C. Santosh ◽  
Chayan Halder ◽  
Nibaran Das ◽  
Kaushik Roy
Keyword(s):  
Handwritten Documents ◽  
Script Identification ◽  
Word Level ◽  
Indic Script

scholarly journals Deep Learning for Word-Level Handwritten Indic Script Identification

2021 ◽  
pp. 499-510
Author(s):  
Soumya Ukil ◽  
Swarnendu Ghosh ◽  
Sk Md Obaidullah ◽  
K. C. Santosh ◽  
Kaushik Roy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Script Identification ◽  
Word Level ◽  
Indic Script

Line Parameter based Word-Level Indic Script Identification System

2016 ◽  
Vol 6 (2) ◽  
pp. 18-41 ◽  
Author(s):  
Pawan Kumar Singh ◽  
Supratim Das ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Hough Transform ◽  
Identification Accuracy ◽  
Distance Transform ◽  
Multi Layer Perceptron ◽  
Script Identification ◽  
Spatial Features ◽  
Word Level ◽  
Line Parameter ◽  
Word Images ◽  
Mlp Classifier

In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts. Since Optical Character Recognition (OCR) engines are usually script-dependent, automatic text recognition in multi-script environment requires a pre-processing module that helps identifying the scripts before processing the same through the respective OCR engine. The work becomes more challenging when it deals with handwritten document which is still a less explored research area. In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Manipuri, Oriya, Urdu, and Roman. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentations are performed at word-level using multiple classifiers on a dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%. The performance of the present technique is also compared with those of other state-of-the-art script identification methods on the same database. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentation are performed at word-level on a total dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%.

Word level multi-script identification

2008 ◽  
Vol 29 (9) ◽  
pp. 1218-1229 ◽  
Author(s):  
Peeta Basa Pati ◽  
A.G. Ramakrishnan
Keyword(s):  
Script Identification ◽  
Word Level
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