Journal ArticleDOI
Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques
Reads0
Chats0
TLDR
Various feature extraction and classification techniques associated with the offline handwriting recognition of the regional scripts are discussed in this survey, which will serve as a compendium not only for researchers in India, but also for policymakers and practitioners in India.Abstract:
Offline handwriting recognition in Indian regional scripts is an interesting area of research as almost 460 million people in India use regional scripts. The nine major Indian regional scripts are Bangla (for Bengali and Assamese languages), Gujarati, Kannada, Malayalam, Oriya, Gurumukhi (for Punjabi language), Tamil, Telugu, and Nastaliq (for Urdu language). A state-of-the-art survey about the techniques available in the area of offline handwriting recognition (OHR) in Indian regional scripts will be of a great aid to the researchers in the subcontinent and hence a sincere attempt is made in this article to discuss the advancements reported in this regard during the last few decades. The survey is organized into different sections. A brief introduction is given initially about automatic recognition of handwriting and official regional scripts in India. The nine regional scripts are then categorized into four subgroups based on their similarity and evolution information. The first group contains Bangla, Oriya, Gujarati and Gurumukhi scripts. The second group contains Kannada and Telugu scripts and the third group contains Tamil and Malayalam scripts. The fourth group contains only Nastaliq script (Perso-Arabic script for Urdu), which is not an Indo-Aryan script. Various feature extraction and classification techniques associated with the offline handwriting recognition of the regional scripts are discussed in this survey. As it is important to identify the script before the recognition step, a section is dedicated to handwritten script identification techniques. A benchmarking database is very important for any pattern recognition related research. The details of the datasets available in different Indian regional scripts are also mentioned in the article. A separate section is dedicated to the observations made, future scope, and existing difficulties related to handwriting recognition in Indian regional scripts. We hope that this survey will serve as a compendium not only for researchers in India, but also for policymakers and practitioners in India. It will also help to accomplish a target of bringing the researchers working on different Indian scripts together. Looking at the recent developments in OHR of Indian regional scripts, this article will provide a better platform for future research activities.read more
Citations
More filters
Journal ArticleDOI
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)
TL;DR: This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.
Journal ArticleDOI
The optical character recognition of Urdu-like cursive scripts
Saeeda Naz,Khizar Hayat,Muhammad Imran Razzak,Muhammad Waqas Anwar,Sajjad A. Madani,Samee U. Khan +5 more
TL;DR: The Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts, with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition in OCR.
Journal ArticleDOI
Handwritten isolated Bangla compound character recognition: A new benchmark using a novel deep learning approach
TL;DR: A novel deep learning technique for the recognition of handwritten Bangla isolated compound character is presented and a new benchmark of recognition accuracy on the CMATERdb 3.3.1.3 dataset is reported.
Posted Content
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)
TL;DR: In this paper, a systematic literature review (SLR) is presented to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions, which serve the purpose of presenting state of the art results and techniques on OCR.
Journal Article
On Recognition of handwritten bangla characters
TL;DR: Multilayer perceptrons (MLP) trained by backpropagation (BP) algorithm are used as classifiers in the present study and results of this study on recognition of handwritten Bangla basic characters will be reported.
References
More filters
Proceedings ArticleDOI
Pre and Post Processing Approaches in Edge Detection for Character Recognition
Binu P. Chacko,P. Babu Anto +1 more
TL;DR: This paper deals with the recognition of handwritten Malayalam characters by presenting edge detection in the preprocessing stage using nonlinear an isotropic diffusion via partial differential equations (PDE) and showing some broken edges.
Proceedings ArticleDOI
Script independent handwritten numeral recognition
TL;DR: This paper presents an off-line handwritten recognition system for numerals that is independent of the script and can be used for any language and particularly for applications like postal address automation.
Proceedings ArticleDOI
Adapting Moments for Handwritten Kannada Kagunita Recognition
Leena R. Ragha,M. Sasikumar +1 more
TL;DR: This paper investigates the use of moments features on Kannada Kagunita by training and testing vowel and consonant features from directional images and cut images on Multi Layer Perceptron with Back Propagation Neural Network.
Proceedings ArticleDOI
A Study on the Effect of Varying Training set Sizes on Recognition Performance with Handwritten Bangla Numerals
TL;DR: A study showing how the recognition performance of an MLP based classifier varies with variation in the training set size is presented in this paper.
Proceedings ArticleDOI
Recognition of handwritten Urdu digits using shape context
TL;DR: This paper presents a fresh approach towards matching and recognition of hand written Urdu digits using the novel descriptor for shape matching, the 'shape context ' recently proposed in the University of California, Berkeley.