Proceedings ArticleDOI
Combined horizontal and vertical projection feature extraction technique for Gurmukhi handwritten character recognition
Manoj Kumar Mahto,Karamjit Bhatia,Rajendra Kumar Sharma +2 more
- pp 59-65
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TLDR
This work proposes a combined horizontal and vertical projection feature extraction scheme for recognition of Gurmukhi characters, an Indic script commonly used in state of Punjab in India.Abstract:
Despite the advancements in Optical Character Recognition (OCR) technologies, problem of Indic script character recognition remains challenging. Especially in case of handwritten characters the challenges are even more. In this work, we focus on off-line recognition of handwritten characters of Gurmukhi, an Indic script commonly used in state of Punjab in India. As a part of this work, we collected a Gurmukhi character dataset of 3500 images. This dataset is collected from 10 writers. We propose a combined horizontal and vertical projection feature extraction scheme for recognition of Gurmukhi characters. We have tested our method on the collected dataset and achieved a high character recognition accuracy of 98.06%.read more
Citations
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Hybrid CNN-SVM Classifier for Handwritten Digit Recognition
Savita Ahlawat,Amit Choudhary +1 more
TL;DR: A hybrid model of a powerful Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) for recognition of handwritten digit from MNIST dataset is developed, which achieves recognition accuracy of 99.28% over MNIST handwritten digits dataset.
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Comparison Between Neural Network and Support Vector Machine in Optical Character Recognition
TL;DR: This experiment achieves the highest accuracy of 94.43% using Support Vector Machine (SVM) classifier with the feature extraction algorithms are projection profile and the combination of zoning + projection profile.
Proceedings ArticleDOI
Malayalam handwritten character recognition using convolutional neural network
TL;DR: The proposed system uses Convolutional neural network to extract features and is tested against a newly constructed dataset of six Malayalam characters, which shows remarkable improvement in recognizing characters of other languages.
Journal ArticleDOI
Segmentation and Recognition of Handwritten Kannada Text Using Relevance Feedback and Histogram of Oriented Gradients – A Novel Approach
Karthik S,Srikanta Murthy K +1 more
TL;DR: A method has been proposed for proper segmentation of the text to improve the performance of OCR at the later stages and the experimentation is delivered promising results.
Journal ArticleDOI
Machine Learning Approaches for recognition of offline Tulu Handwritten Scripts
C K Savitha,P J Antony +1 more
TL;DR: Comparative analysis shows that Deep CNN gives higher efficiency compared with shallow learning techniques for isolated Tulu characters from modern documents and 80.49% for isolated character from Tulu palm leaf manuscripts.
References
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TL;DR: This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters in terms of invariance properties, reconstructability and expected distortions and variability of the characters.
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Proceedings ArticleDOI
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TL;DR: This paper presents an efficient offline handwritten Gurmukhi character recognition system based on diagonal features and transitions features using k-NN classifier, which achieves a maximum recognition accuracy of 94.12% and is presented in this paper.
Journal ArticleDOI
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TL;DR: The work presented in this thesis, focuses on the problem of recognition of isolated handwritten characters in Gurmukhi script using the feature extraction method Zoning and the Support Vector Machine, a learning machine with very good generalization ability.
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