Image augmentation by blocky artifact in Deep Convolutional Neural Network for handwritten digit recognition

Shopon,; Mohammed, Nabeel; Abedin, Anowarul

doi:10.1109/icivpr.2017.7890867

Cited by 22 publications

(11 citation statements)

References 23 publications

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“…The accuracy (99.55%) of the proposed method exceeds the best accuracy (99.35%) reported in the literature Fig. 4 The performance curve (training and validation accuracies) of the VGG-11M architecture on the ISI dataset for each iteration at resolution 29 × 29 on ISI dataset [43]. Finally, the confusion matrix of the VGG-11M model on the test set (each class of numeral contains 400 samples) of the ISI database at resolution 32 × 32 is shown in Fig.…”

Section: Resultsmentioning

confidence: 77%

“…The accuracy of a classifier can be affected by both the image resolution and the sample used to test the classifier. The comparison of the proposed method and the method [43] (best accuracy 99.35%) on the same test set showed that our method is about 0.45% more accurate greater than the previous best method. The development of the VGG-11M model opens the doors for the future research on Bengali handwritten character recognition and related problems.…”

Section: Discussionmentioning

confidence: 85%

“…How much the resolution affects the classification performance was also studied. The comparison of the proposed VGG-11M model with the image augmentation by blocky artifact and deep convolutional neural network [43] model (i.e., the method with the highest accuracy) was in favor of the new method. The ISI dataset proved more robust even with small sets of a sample.…”

Section: Resultsmentioning

confidence: 95%

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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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Section: Resultsmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 95%

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Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

et al. 2019

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“…On another paper, we see the existence of auto-encoder for unsupervised pre-training through Deep CNN, which consists of more than one hidden layer, with 3 convolutional layers, each layer followed by 2 × 2 max pool layer. This scheme [20] proposed by Md Shopon et.al. The layers have 32 × 3 × 3 number of kernels. In the same manner, the decoder has an architecture with each convolutional layer with 5 neurons, rather than 32.…”

Section: Existing Work On Bengali Handwritten Numeral Recognitionmentioning

confidence: 99%

BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks

Sufian

Ghosh

Naskar

et al. 2022

Journal of King Saud University - Computer and Information Scie

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Bengali handwritten digit recognition can be done using different image classification techniques. But the images of handwritten digits are different from natural images as the orientation of a digit as well as similarity of features of different digits are important. On the other hand, deep convolutional neural networks are achieving huge success in computer vision problems, especially in image classification. This BDNet is a densely connected deep convolutional neural network model based on state-of-the-art algorithm DenseNet to classify Bengali handwritten numeral digits. The BDNet has end-to-end trained using ISI Bengali handwritten numeral dataset with 5-fold cross-validation. The BD-Net has achieved a test accuracy of 99.65%(baseline was 99.40%) on test data of ISI Bengali handwritten numerals. The trained model also gives 97.50% on own created dataset(which are not used during training). That is, this model gives a 41.66% error reduction compared to the previous state-of-the-art model. Codes, trained model and own dataset available at: https://github.com/Sufianlab/BDNet.

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“…As depicted in the figure, the model not only rightly identifies the subject to be a boy, it also accurately describes what the subject is wearing ( a bow tie ). While there has been a lot of recent interest in using machine learning on Bangla isolated characters [5], [6], [7], [8], there has been no significant work on generating Bangla image captions. Taking into account the present state and the challenges, this paper reports the development of "Chittron", an automatic image annotating system in Bangla.…”

Section: Introductionmentioning

confidence: 99%

Chittron: An Automatic Bangla Image Captioning System

Rahman

Mohammed

Mansoor

et al. 2019

Procedia Computer Science

Self Cite

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Automatic image caption generation aims to produce an accurate description of an image in natural language automatically. However, Bangla, the fifth most widely spoken language in the world, is lagging considerably in the research and development of such domain. Besides, while there are many established data sets to related to image annotation in English, no such resource exists for Bangla yet. Hence, this paper outlines the development of "Chittron", an automatic image captioning system in Bangla. Moreover, to address the data set availability issue, a collection of 16, 000 Bangladeshi contextual images has been accumulated and manually annotated in Bangla. This data set is then used to train a model which integrates a pre-trained VGG16 image embedding model with stacked LSTM layers. The model is trained to predict the caption when the input is an image, one word at a time. The results show that the model has successfully been able to learn a working language model and to generate captions of images quite accurately in many cases. The results are evaluated mainly qualitatively. However, BLEU scores are also reported. It is expected that a better result can be obtained with a bigger and more varied data set.

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Image augmentation by blocky artifact in Deep Convolutional Neural Network for handwritten digit recognition

Cited by 22 publications

References 23 publications

Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks

Chittron: An Automatic Bangla Image Captioning System

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