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

Alom, Zahangir; Sidike, Paheding; Hasan, Mahmudul; Taha, Tarek M.; Asari, Vijayan K.

doi:10.1155/2018/6747098

Cited by 85 publications

(36 citation statements)

References 65 publications

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“…There is a good survey on DL approaches for image processing and computer vision related tasks, including image classification, segmentation, and detection [102]. For examples, single image super-resolution using CNN method [103], image denoising using block-matching CNN [104], photo aesthetic assessment using A-Lamp (Adaptive Layout-Aware Multi-Patch Deep CNN) [105], DCNN for hyperspectral imaging segmentation [106], image registration [107], fast artistic style transfer [108], image background segmentation using DCNN [109], handwritten character recognition [110], optical image classification [111], crop mapping using high-resolution satellite imagery [112], object recognition with cellular simultaneous recurrent networks and CNN [113]. The DL approaches are massively applied to human activity recognition tasks and achieved state-of-the-art performance compared to exiting approaches [114][115][116][117][118][119].…”

Section: Image Processing and Computer Visionmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Theory and Architectures

et al. 2019

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In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

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Section: Image Processing and Computer Visionmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Theory and Architectures

et al. 2019

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“…This model of CNN has been used extensively in some image recognition problems since it was introduced in 2014 by [25]. The authors of [32] have then explored its performance for Bengali handwritten character recognition. In our model, we have incorporated some extra layers, like batch normalization, zero padding, dropout and a favorable selection of the number of convolution layers, max-pooling layer, and the number of filters that boosted the classification performance by a significant margin.…”

Section: Discussionmentioning

confidence: 99%

“…Simonyan and Zisserman [25] in 2014 introduced a model of CNN to investigate the effect of convolutional depth on its accuracy in large-scale image recognition problem and named it VGG according to the name of their research group: Visual Geometry Group. Two special architectures of VGG, VGG-11 [31] and VGG-16 [32] were used for image segmentation and handwritten Bengali characters recognition, respectively. The architecture of VGG-11 consists of 8 convolutional layers and 3 fully connected layers followed by a single Softmax layer, and the architecture of VGG-16 consists of 13 convolutional layers and 3 fully connected layers followed by a single Softmax layer.…”

Section: Vggmentioning

confidence: 99%

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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“…A deep convolution neural network (DCNN) is a multilayer neural network that is performed as a deep supervised learning method [15]. The DCNN has achieved excellent performances on image recognition tasks for the last few years [7,8,16,17]. It can perform both feature extraction and image classification tasks [15].…”

Section: Dcnn Classifiermentioning

confidence: 99%

An automated eye disease recognition system from visual content of facial imagesusing machine learning techniques

Akram¹,

Debnath²

2020

Turk J Elec Eng & Comp Sci

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Many eye diseases like cataracts, trachoma, or corneal ulcer can cause vision problems. Progression of these eye diseases can only be prevented if they are recognized accurately at the early stage. Visually observable symptoms differ a lot among these eye diseases. However, a wide variety of symptoms is necessary to be analyzed for the accurate detection of eye diseases. In this paper, we propose a novel approach to provide an automated eye disease recognition system using visually observable symptoms applying digital image processing techniques and machine learning techniques such as deep convolution neural network (DCNN) and support vector machine (SVM). We apply the principal component analysis and t-distributed stochastic neighbor embedding methods for better feature selection. The proposed system automatically divides the facial components from the frontal facial image and extracts the eye part. The proposed method analyzes and classifies seven eye diseases including cataracts, trachoma, conjunctivitis, corneal ulcer, ectropion, periorbital cellulitis, and Bitot's spot of vitamin A deficiency. From the experimental results, we see that the DCNN model outperforms SVM models. We also compare our method with some other existing methods. Our method shows improved accuracy compared to other methods. The average accuracy rate of our DCNN model is 98.79% with sensitivity of 97% and specificity of 99%.

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

Cited by 85 publications

References 65 publications

A State-of-the-Art Survey on Deep Learning Theory and Architectures

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

An automated eye disease recognition system from visual content of facial imagesusing machine learning techniques

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