scholarly journals Multilingual Conversation ASCII to Unicode in Indic Script

2017 ◽  
Author(s):  
Rajwinder Singh ◽  
Charanjiv Singh Saroa
Keyword(s):  
Indic Script

scholarly journals A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

2021 ◽  
Author(s):  
Ritam Guha ◽  
Manosij Ghosh ◽  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Feature Selection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Classification Problem ◽  
Classification Model ◽  
Support Vector ◽  
Intermediate Step ◽  
Hybrid Swarm ◽  
Feature Vectors ◽  
Indic Script

AbstractIn any multi-script environment, handwritten script classification is an unavoidable pre-requisite before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimensions, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In the present work, we have addressed this issue by introducing a new FS algorithm, called Hybrid Swarm and Gravitation-based FS (HSGFS). This algorithm has been applied over three feature vectors introduced in the literature recently—Distance-Hough Transform (DHT), Histogram of Oriented Gradients (HOG), and Modified log-Gabor (MLG) filter Transform. Three state-of-the-art classifiers, namely, Multi-Layer Perceptron (MLP), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM), are used to evaluate the optimal subset of features generated by the proposed FS model. Handwritten datasets at block, text line, and word level, consisting of officially recognized 12 Indic scripts, are prepared for experimentation. An average improvement in the range of 2–5% is achieved in the classification accuracy by utilizing only about 75–80% of the original feature vectors on all three datasets. The proposed method also shows better performance when compared to some popularly used FS models. The codes used for implementing HSGFS can be found in the following Github link: https://github.com/Ritam-Guha/HSGFS.

A clustering‐based feature selection framework for handwritten Indic script classification

2019 ◽  
Vol 36 (6) ◽  
Author(s):  
Iman Chatterjee ◽  
Manosij Ghosh ◽  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Feature Selection ◽  
Selection Framework ◽  
Indic Script

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 A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition

2018 ◽  
Vol 4 (2) ◽  
pp. 39 ◽  
Author(s):  
Anirban Mukhopadhyay ◽  
Pawan Singh ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Classifier Combination ◽  
Script Recognition ◽  
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
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