Bangla Handwritten Digit Recognition Using an Improved Deep Convolutional Neural Network Architecture

Saha, Chandrika; Faisal, Rahat Hossain; Rahman, Md. Mostafijur

doi:10.1109/ecace.2019.8679309

Cited by 19 publications

(8 citation statements)

References 46 publications

(55 reference statements)

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“…A filter is placed on the image resulting in a convolved feature map, analogous to watching a specific section of an outdoor scene through a window, which makes specific features prominent. Extracting of image features is the important use of this layer [12]. On convoluting the m × m filter over the n × n input neurons of the input layer, (n-m+1) x (n-m+1) is delivered as output.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

Classification of MNIST Image Dataset Using Improved Convolutional Neural Network

Chattyopadhyay¹

2022

IJRASET

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Abstract— Convolutional Neural Network (CNN) holds the current research interest in the ever-evolving image classification field. Accurate classifying the image data with minimum of time is highly desired. But the traditional CNN architecture often fails to generate the appropriate outcome for large dataset. So, a modified approach of CNN is proposed here which is the combination of data augmentation and batch normalization embedded with CNN. Now a days identifying or classifying digits accurately with variety of modes is really a task of challenge. The advantages of the proposed approach are noted when it is applied to the popular MNIST dataset used for digit classification. The proposed approach has been compared with some existing techniques and results infer that the validation, training loss and testing accuracy of the proposed approach are more superior as compared to the state-of-art approaches.

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Section: A Convolutional Neural Networkmentioning

confidence: 99%

Classification of MNIST Image Dataset Using Improved Convolutional Neural Network

Chattyopadhyay¹

2022

IJRASET

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“…Chowdhury et al [9], for the task of recognising Bengali digits, proposed use of convolutional neural networks and obtained accuracy of 95.25%. Saha et al [39] proposed a seven layered deep convolutional neural network (D-CNN) architecture and achieved an accuracy of 97.6% on the collected dataset.…”

Section: Handwritten Digit Recognitionmentioning

confidence: 99%

Handwritten Digit Recognition Using Bayesian ResNet

et al. 2021

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The problem of handwritten digit recognition has seen various developments in the recent times, especially in neural network domain. The methods based on neural network work quite effectively for the seen classes of data by providing deterministic results. However, these methods tend to behave in similar fashion even for unseen class of data. For example, a neural network trained on English language digits will give a deterministic prediction even when tested on digits of other languages. Hence, it is required to predict uncertainty for such methods in this scenario. In this paper, we employ Bayesian inference into the existing ResNet18 framework to bring out uncertainty for handwritten digit recognition when there is a new class of test digit. We term the new architecture as B-ResNet. The novel B-ResNet is first of its kind to be investigated for the handwritten digit recognition. Various experiments on datasets of English, Devanagari, Gujarati, Bengali digits and their all possible combinations demonstrate the efficiency and performance of the B-ResNet for hand written digit recognition.

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“…Kumar Reddy et al proposed a method of handwritten Sanskrit digital recognition based on CNN and RMSprop optimization technology [17]. Saha et al developed a method to recognize handwritten Bangla digits based on neural network frameworks of different depths and achieved good results [18]. To solve the problem of large demand for computing resources in convolutional neural networks, Tan and Le proposed a MixNet network.…”

Section: Related Researchmentioning

confidence: 99%

Offline handwritten Tai Le character recognition using ensemble deep learning

et al. 2021

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Handwriting recognition is an important area in pattern recognition. For many years, Tai Le has been widely used in Southwest China and Southeast Asia, which makes it of great interest for recognition research. The characteristics of the highly similar characters in Tai Le, such as its large proportion of similar characters and the randomness of its writing, bring great challenges to the task of recognition. In this paper, a method based on ensemble deep learning for offline handwritten Tai Le characters is proposed. First, the handwritten Tai Le character dataset SDH2019.2 was constructed and preprocessed. Then, an ensemble deep convolutional neural network (EDCNN) model was constructed by using a stacking strategy. Thirty deep neural network (DNN) and logistic regression algorithms were integrated into a strong Tai Le classifier by stacking. Experiments showed that the proposed model is competitive with the base DNN model and other ensemble models. The results indicate that the performance of Tai Le recognition by the stacking ensemble-based deep neural network model is high, with an accuracy of 98.85%. Additionally, its precision, recall and F1-score of 98.87%, 98.85% and 98.85%, respectively, are superior to those of other classic neural network models. To verify the general applicability of EDCNN, its effectiveness was also verified by recognizing MNIST handwritten digits and Devanagari handwritten characters.

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Bangla Handwritten Digit Recognition Using an Improved Deep Convolutional Neural Network Architecture

Cited by 19 publications

References 46 publications

Classification of MNIST Image Dataset Using Improved Convolutional Neural Network

Classification of MNIST Image Dataset Using Improved Convolutional Neural Network

Handwritten Digit Recognition Using Bayesian ResNet

Offline handwritten Tai Le character recognition using ensemble deep learning

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