Bangla handwritten digit recognition using autoencoder and deep convolutional neural network

Shopon,; Mohammed, Nabeel; Abedin, Anowarul

doi:10.1109/iwci.2016.7860340

Cited by 67 publications

(20 citation statements)

References 15 publications

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“…Maitra et al [14] used the classical LeNet-5 CNN model in the identification of five kinds of handwritten numbers, namely, Bangla, Devanagari, English, Oriya, and Telugu. For Bangla handwritten digit recognition only, auto-encoder and deep convolutional neural network are both explored [15].…”

Section: Related Workmentioning

confidence: 99%

Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Jiang

Zhang

2020

IEICE Trans. Inf. & Syst.

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Handwritten numeral recognition is a classical and important task in the computer vision area. We propose two novel deep learning models for this task, which combine the edge extraction method and Siamese/Triple network structures. We evaluate the models on seven handwritten numeral datasets and the results demonstrate both the simplicity and effectiveness of our models, comparing to baseline methods.

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Section: Related Workmentioning

confidence: 99%

Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Jiang

Zhang

2020

IEICE Trans. Inf. & Syst.

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“…These factors affect the classification accuracy negatively. Some researchers have used convolutional neural network in their works [6] [7] [8]. But some of the works do not consider all the character classes.…”

Section: Introductionmentioning

confidence: 99%

Bangla Handwritten Character Recognition Using Extended Convolutional Neural Network

Das¹,

Hasan²,

Jani³

et al. 2021

JCC

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The necessity of recognizing handwritten characters is increasing day by day because of its various applications. The objective of this paper is to provide a sophisticated, effective and efficient way to recognize and classify Bangla handwritten characters. Here an extended convolutional neural network (CNN) model has been proposed to recognize Bangla handwritten characters. Our CNN model is tested on "BanglalLekha-Isolated" dataset where there are 10 classes for digits, 11 classes for vowels and 39 classes for consonants. Our model shows accuracy of recognition as: 99.50% for Bangla digits, 93.18% for vowels, 90.00% for consonants and 92.25% for combined classes.

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“…Research on handwritten numerals has made impressive progress in some languages such as Arabic, Chinese, and English [1,[3][4][5]. The automatic recognition of printed Bengali numerals is also very high; however, the progress of handwritten Bengali numeral recognition (HBNR) is far behind these languages [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…By this time, CNNs have been recognized as the best classification method for handwritten (English or Hindu-Arabic) digits as claimed in the survey done by [18]. However, a few researchers [7,10,19,20] have explored the power of CNNs for handwritten Bengali numerals recognition. One major requirement of using CNNs is that it requires huge number of samples for training; however, none of the existing works explored the power of CNNs on the recently published large NUMTADB dataset.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results showed that the CNNs outperformed the standard shallow learning methods with predefined features. The authors of [10] used autoencoders and deep CNNs for recognition of handwritten Bengali numerals. The deep network was trained with 19313 samples of the ISI training dataset and tested with images of the CMATERdb3.1.1 dataset, and they leading to an accuracy of 99.50%.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

et al. 2019

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Handwritten recognition has drawn profound attention since decades ago due to its numerous potential applications in real life. Research on unconstrained handwritten recognition in some languages has achieved attractive advancement, but it lags behind for Bengali even though it is the major language spoken by about 230 million people in the Indian subcontinent, and even the first and official language of Bangladesh. Recently, the use of convolutional neural network (CNN) has been reported with high accuracy in pattern recognition and computer vision problems. The main purpose of this study is to provide an architecture of a CNN to improve the accuracy of handwritten Bengali numerals recognition (HBNR) and compare its performance with the existing ones. We proposed a new CNN architecture, VGG-11M, which improves an existing one (VGG-11). The normalized and rescaled images of each numeral were augmented by different transformation operations to increase the training samples and to add diversity in the dataset. Then, the images were used to train the proposed VGG-11M model. The recognition accuracy of the developed system was tested on both training and test sets of three publicly available handwritten Bengali numerals database at different resolutions. Finally the performance of the model was compared with four other architectures (LeNet-5, ResNet-50, VGG-11, and VGG-16). The highest accuracy 99.80%, 99.66%, and 99.25% was obtained using the proposed architecture on the test set of ISI, CMATERDB, and NUMTADB dataset, respectively, at resolution 32 × 32. The proposed VGG-11M outperformed the existing architectures of CNN on HBNR.

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Bangla handwritten digit recognition using autoencoder and deep convolutional neural network

Cited by 67 publications

References 15 publications

Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Bangla Handwritten Character Recognition Using Extended Convolutional Neural Network

Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

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