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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | IDENTIFICATION AND CONVERSION OF HANDWRITTEN MALAYALAM SCRIPTS USING CONVOLUTIONAL NEURAL NETWORKS |
| Authors Name: | Joslin Johnson , Catherine Davis , Ashly Raphel , Mrs Soumya Varma |
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IJRTI_170052
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| Published Paper Id: | IJRTI1703022 |
| Published In: | Volume 2 Issue 3, March-2017 |
| DOI: | |
| Abstract: | Handwritten character recognition is still a research challenge in OCR discipline, especially for Indian scripts. The recognition of handwriting however, is still considered as an open research problem due to its substantial variation in appearance. The goal of OCR is to classify optical patterns in an image to the corresponding characters. There are different OCR techniques like Online Handwritten Malayalam Character Recognition using LIBSVM in Matlab. Here real time (x,y) coordinates per stroke are acquired and preprocessed. Directional and Curvature features are extracted and trained in LIBSVM, a tool for SVM Classifers. Testing alphabet is given online to the trained SVM network and the recognized label is displayed in Notepad. Another method proposed is a two-stage approach. The first stage is a group classifier, where a group consists of similar characters and those that misclassify among themselves. In the second stage, a character assigned to a group in the first stage is classified to a particular character class. Another method proposed is Off-line Handwritten Character Recognition using Hidden Markov Model. Training and recognition are performed using Hidden Markov Model Toolkit. Recognition process involves several steps including image acquisition, dataset preparation, pre-processing, feature extraction, training and recognition. An average accuracy of about 81.38% has been obtained. Thus the proposed system is done in python using convolutional neural network. It takes less processing time and has an accuracy of 95%. |
| Keywords: | Optical Character Recognition, Support Vector Machine, Convolutional Neural Network, Machine Learning |
| Cite Article: | "IDENTIFICATION AND CONVERSION OF HANDWRITTEN MALAYALAM SCRIPTS USING CONVOLUTIONAL NEURAL NETWORKS", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.2, Issue 3, page no.103 - 112, March-2017, Available :http://www.ijrti.org/papers/IJRTI1703022.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI1703022
Registration ID:170052
Published In: Volume 2 Issue 3, March-2017
DOI (Digital Object Identifier):
Page No: 103 - 112 Country: Thrissur, Kerala, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI1703022 Published Paper PDF: https://www.ijrti.org/papers/IJRTI1703022 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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