Handwritten Bangla digit recognition using Sparse Representation Classifier

Khan, Haider Adnan; Helal, Abdullah Al; Ahmed, Khawza I.

doi:10.1109/iciev.2014.6850817

Cited by 37 publications

(20 citation statements)

References 12 publications

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“…Khan et al [28] presented a framework for Bangla digit recognition using a sparse representation classifier (SRC). They used the zone density feature extraction method for extracting only local information from differently sized zoning, and zone density features were calculated as the normalized number of foreground pixels in each zone.…”

Section: Literature Studymentioning

confidence: 99%

M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification

Guha

Ghosh

Singh

et al. 2019

Journal of Intelligent Systems

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Abstract The feature selection process is very important in the field of pattern recognition, which selects the informative features so as to reduce the curse of dimensionality, thus improving the overall classification accuracy. In this paper, a new feature selection approach named Memory-Based Histogram-Oriented Multi-objective Genetic Algorithm (M-HMOGA) is introduced to identify the informative feature subset to be used for a pattern classification problem. The proposed M-HMOGA approach is applied to two recently used feature sets, namely Mojette transform and Regional Weighted Run Length features. The experimentations are carried out on Bangla, Devanagari, and Roman numeral datasets, which are the three most popular scripts used in the Indian subcontinent. In-house Bangla and Devanagari script datasets and Competition on Handwritten Digit Recognition (HDRC) 2013 Roman numeral dataset are used for evaluating our model. Moreover, as proof of robustness, we have applied an innovative approach of using different datasets for training and testing. We have used in-house Bangla and Devanagari script datasets for training the model, and the trained model is then tested on Indian Statistical Institute numeral datasets. For Roman numerals, we have used the HDRC 2013 dataset for training and the Modified National Institute of Standards and Technology dataset for testing. Comparison of the results obtained by the proposed model with existing HMOGA and MOGA techniques clearly indicates the superiority of M-HMOGA over both of its ancestors. Moreover, use of K-nearest neighbor as well as multi-layer perceptron as classifiers speaks for the classifier-independent nature of M-HMOGA. The proposed M-HMOGA model uses only about 45–50% of the total feature set in order to achieve around 1% increase when the same datasets are partitioned for training-testing and a 2–3% increase in the classification ability while using only 35–45% features when different datasets are used for training-testing with respect to the situation when all the features are used for classification.

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Section: Literature Studymentioning

confidence: 99%

M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification

Guha

Ghosh

Singh

et al. 2019

Journal of Intelligent Systems

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“…Xu et al [ 23 ] used a hierarchical Bayesian network which directly takes raw images as the network inputs and classifies them using a bottom-up approach. Sparse representation classifier has also been applied for Bangla digit recognition [ 4 ], where 94% accuracy was reported for handwritten digit recognition. In [ 6 ], handwritten Bangla basic and compound character recognition using multilayer perceptron (MLP) [ 24 ] and SVM classifier was suggested, while handwritten Bangla numeral recognition using MLP was presented in [ 7 ] where the average recognition rate reached 96.67%.…”

Section: Related Workmentioning

confidence: 99%

“…Bangla is one of the most spoken languages and ranked fifth in the world and spoken by more than 200 million people [ 4 , 5 ]. It is the national and official language of Bangladesh and the second most popular language in India.…”

Section: Introductionmentioning

confidence: 99%

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Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks

Alom

Sidike

Hasan

et al. 2018

Computational Intelligence and Neuroscience

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In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.

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“…Bangla is one of the prestigious languages all over the world. About 230 million people from around the world speak Bangla as their native language ( Khan, Al Helal & Ahmed, 2014 ), and approximately 37 million people use it as a second language for both speaking and writing purposes. Thus, by the total number of speakers worldwide, Bangla is the fifth most spoken native language ( Alom et al, 2018 ) and the seventh most spoken language as well.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based ensemble methods to recognize Bangla handwritten character

Shibly¹,

Tisha²,

Tani³

et al. 2021

PeerJ Computer Science

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In this era of advancements in deep learning, an autonomous system that recognizes handwritten characters and texts can be eventually integrated with the software to provide better user experience. Like other languages, Bangla handwritten text extraction also has various applications such as post-office automation, signboard recognition, and many more. A large-scale and efficient isolated Bangla handwritten character classifier can be the first building block to create such a system. This study aims to classify the handwritten Bangla characters. The proposed methods of this study are divided into three phases. In the first phase, seven convolutional neural networks i.e., CNN-based architectures are created. After that, the best performing CNN model is identified, and it is used as a feature extractor. Classifiers are then obtained by using shallow machine learning algorithms. In the last phase, five ensemble methods have been used to achieve better performance in the classification task. To systematically assess the outcomes of this study, a comparative analysis of the performances has also been carried out. Among all the methods, the stacked generalization ensemble method has achieved better performance than the other implemented methods. It has obtained accuracy, precision, and recall of 98.68%, 98.69%, and 98.68%, respectively on the Ekush dataset. Moreover, the use of CNN architectures and ensemble methods in large-scale Bangla handwritten character recognition has also been justified by obtaining consistent results on the BanglaLekha-Isolated dataset. Such efficient systems can move the handwritten recognition to the next level so that the handwriting can easily be automated.

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Handwritten Bangla digit recognition using Sparse Representation Classifier

Cited by 37 publications

References 12 publications

M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification

M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification

Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks

Convolutional neural network-based ensemble methods to recognize Bangla handwritten character

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