Handwritten Bangla character and numeral recognition using convolutional neural network for low-memory GPU

Keserwani, Prateek; Ali, Tofik; Roy, Partha Pratim

doi:10.1007/s13042-019-00938-1

Cited by 22 publications

(5 citation statements)

References 29 publications

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“…For the Bangla script datasets, viz, D1, D2, D3, and D4, the deep learning based techniques used to compare our results are unified CNN [40], AlexNet [41], DenseNet [43], Multi-column Multi-scale CNN (MMCNN) [45], Residual Network (ResNet-50) [47], BornoNet [48], and modified ResNet-18 [49]. Moreover, for the Devanagari script datasets, viz, D5 and D6, the deep learning based techniques used to compare our results are MMCNN [45], CNN [50], ResNet-50 [51], and Inception V3 [52].…”

Section: 69mentioning

confidence: 99%

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

Sharma¹,

Kaushik²,

Gondhi³

et al. 2022

Computers, Materials &Amp; Continua

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Even though several advances have been made in recent years, handwritten script recognition is still a challenging task in the pattern recognition domain. This field has gained much interest lately due to its diverse application potentials. Nowadays, different methods are available for automatic script recognition. Among most of the reported script recognition techniques, deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms. However, the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error, which renders them unfeasible. This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources. To alleviate this shortcoming, this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization (QPSO), which is capable of automatically evolving the meaningful convolutional neural network (CNN) topologies. The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts, namely Bangla, Devanagari, and Dogri, consisting of handwritten characters and digits. Empirically, the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.

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Section: 69mentioning

confidence: 99%

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

Sharma¹,

Kaushik²,

Gondhi³

et al. 2022

Computers, Materials &Amp; Continua

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“…Recently, convolutional neural networks (CNNs) have emerged as a promising deep learning based approach for the recognition of handwritten numerals (Keserwani et al, 2019;Rabby et al, 2019;Sufian et al, 2020). The work described by Ashiquzzaman and Tushar (2017) proposed a method based on a CNN which achieved an accuracy of 97.4% when tested on the CMATERdb3.3.1 (CMATER Dataset, 2018) Arabic numeral database.…”

Section: Existing Workmentioning

confidence: 99%

A new feature extraction approach for script invariant handwritten numeral recognition

et al. 2021

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Handwritten numeral recognition is a challenging research problem because of the enormous varieties of styles in which human beings write the numerals. Several researchers have tried to find solutions to this problem with exceptional recognition accuracies. However, most of these solutions have been dedicated to single script numerals. Such methods are inappropriate for multi-lingual nations such as India where a large number of scripts are used. Keeping this issue in mind, a new feature descriptor named symbolization of binary images (SBI) is introduced here for the recognition of handwritten numerals of different scripts. Effectiveness of SBI is supported with experiments showing its script-invariant nature. Classification of numerals using a multiclass support vector machine (SVM) classifier yields the recognition accuracies of 98.18, 96.22, 96.52, and 95.53% on datasets of numerals written in four popular scripts of the world: Arabic, Bangla, Devanagari, and Latin, respectively. This scheme has also been extended to the situation when the script used is not known a priori or the numerals written in a document belong to pairs of mixed scripts of {Arabic, Devanagari, Bangla} with Latin producing recognition rates of 92.97, 91.25, and 91.67%, respectively. When all four scripts are mixed, the recognition rate is still 90.98% overall. Encouraging outcomes suggest that the proposed SBI feature descriptor can recognize numerals invariant of the script class.

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“…In [8], they study a common difficulty often faced by researchers exploring handwriting recognition in lowresource scripts and try to overcome the limitations of generic data augmentation strategies by proposing a modular deformation network that is trained to learn a manifold of parameters seeking to deform the features learned by the original task network. By the availability of GPU with limited memory and computing resources, researchers propose an efficient deep architecture having a limited number of parameters, which can be trained on a low memory GPU for character recognition [9].…”

Section: Related Workmentioning

confidence: 99%

Training Data Sets Construction from Large Data Set for PCB Character Recognition

Fabrice¹,

Gang²,

Lee³

2019

J Multimed Inf Syst

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Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.

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Handwritten Bangla character and numeral recognition using convolutional neural network for low-memory GPU

Cited by 22 publications

References 29 publications

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

A new feature extraction approach for script invariant handwritten numeral recognition

Training Data Sets Construction from Large Data Set for PCB Character Recognition

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