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%.

scholarly journals A Baybayin word recognition system

2021 ◽  
Vol 7 ◽  
pp. e596
Author(s):  
Rodney Pino ◽  
Renier Mendoza ◽  
Rachelle Sambayan
Keyword(s):  
Basic Education ◽  
Recognition Accuracy ◽  
Recognition System ◽  
The Philippines ◽  
Support Vector ◽  
Writing System ◽  
Review Of The Literature ◽  
Word Level ◽  
Latin Script ◽  
Word Images

Baybayin is a pre-Hispanic Philippine writing system used in Luzon island. With the effort in reintroducing the script, in 2018, the Committee on Basic Education and Culture of the Philippine Congress approved House Bill 1022 or the ”National Writing System Act,” which declares the Baybayin script as the Philippines’ national writing system. Since then, Baybayin OCR has become a field of research interest. Numerous works have proposed different techniques in recognizing Baybayin scripts. However, all those studies anchored on the classification and recognition at the character level. In this work, we propose an algorithm that provides the Latin transliteration of a Baybayin word in an image. The proposed system relies on a Baybayin character classifier generated using the Support Vector Machine (SVM). The method involves isolation of each Baybayin character, then classifying each character according to its equivalent syllable in Latin script, and finally concatenate each result to form the transliterated word. The system was tested using a novel dataset of Baybayin word images and achieved a competitive 97.9% recognition accuracy. Based on our review of the literature, this is the first work that recognizes Baybayin scripts at the word level. The proposed system can be used in automated transliterations of Baybayin texts transcribed in old books, tattoos, signage, graphic designs, and documents, among others.

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

Using focus of attention with the hough transform for accurate line parameter estimation

1994 ◽  
Vol 27 (9) ◽  
pp. 1127-1134 ◽  
Author(s):  
P.L. Palmer ◽  
J. Kittler ◽  
M. Petrou
Keyword(s):  
Parameter Estimation ◽  
Hough Transform ◽  
Focus Of Attention ◽  
Line Parameter

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

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

Script identification of handwritten word images

2009 ◽  
Author(s):  
Anurag Bhardwaj ◽  
Huaigu Cao ◽  
Venu Govindaraju
Keyword(s):  
Script Identification ◽  
Word Images
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