Book ChapterDOI
Handwritten Bengali Character Recognition Using Deep Convolution Neural Network
Suprabhat Maity,Anirban Dey,Ankan Chowdhury,Abhijit Banerjee +3 more
- pp 84-92
TLDR
This paper uses 15,000 instances of Bengali alphabets to create a recognition model, which when provided with images of physical pieces of handwritten texts, is able to segment and extract characters from the said image of a physical handwritten text with 65% accuracy and recognize the properly segmented alphABets with 99.5% accuracy.Abstract:
Recognition of Handwritten Character had been one of the promising area of research for its applications in diverse field, it appear to be a challenging research In our paper, we focus specifically on offline handwritten character recognition of regional language (Bengali) by first detecting individual characters The principal approaches for offline handwritten character recognition may be divided into two classes, segmentation and holistic based In our method we applied segmentation based handwritten word recognition and to identify individual characters neural network have been used We have used 15,000 instances of Bengali alphabets to create a recognition model, which when provided with images of physical pieces of handwritten texts, it is able to segment and extract characters from the said image of a physical handwritten text with 65% accuracy, and recognize the properly segmented alphabets with 995% accuracyread more
Citations
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Book ChapterDOI
Quantitative Analysis of Deep CNNs for Multilingual Handwritten Digit Recognition
Mohammad Reduanul Haque,Md. Gausul Azam,Sarwar Mahmud Milon,Md. Shaheen Hossain,Md. Al-Amin Molla,Mohammad Shorif Uddin +5 more
TL;DR: In this paper, the authors examined the performance of ten state-of-the-art deep CNN methods for the recognition of handwritten digits using four most common languages in the Indian sub-continent that creates the foundation of a script invariant handwritten digit recognition system.
Journal ArticleDOI
An Approach to Pattern Recognition for Identification of Devnagari Script Based on Fingertips and Palm
TL;DR: An finger point based signed language symbol to text identification and classification algorithm that is based upon RGB image datasets that provides an excellent classification rate which promises upliftment for research in the upcoming future is presented.
Proceedings ArticleDOI
Hog Features Based Handwritten Bengali Numerals Recognition Using SVM Classifier: A Comparison with Hopfield Implementation
TL;DR: In this paper , the authors implemented a Histogram of Oriented Gradient (HOG) features with Support Vector Machine (SVM) classifier and a Hopfield model to recognize handwritten Bengali numerals into the class of ten segments.
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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A novel hybrid CNN-SVM classifier for recognizing handwritten digits
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