Hierarchical Convolutional Neural Network for Handwritten Digits Recognition

Kayumov, Zufar; Tumakov, Dmitrii; Mosin, Sergey

doi:10.1016/j.procs.2020.04.206

Cited by 26 publications

(8 citation statements)

References 17 publications

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“…On the other hand, the obtained quantitative recognition performance metrics’ precision, recall, and F-measure on this benchmark had the same value of 0.9968%. These results surpassed that of many recent dedicated state-of-the-arts methods [ 101 , 102 , 103 , 104 ] evaluated on this database.…”

Section: Experiments and Discussionmentioning

confidence: 63%

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Ahmed

Gogate

Tahir

et al. 2021

Entropy

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Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model.

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Section: Experiments and Discussionmentioning

confidence: 63%

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Ahmed

Gogate

Tahir

et al. 2021

Entropy

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“…Under the background of the comprehensive development of global inform ionization and the improvement of automation, the demand for digital recognition of rival writings has become very urgent. Given the limitations of the handwritten numeral recognition method, this paper based on artificial intelligence and big data technology enterprise project, in combination with convolution the advantage of neural network in image classification [14][15] [16] processing, put forward how to use handwritten digital convolution neural network classification and recognition, and how to through improved convolution neural network model, made in the implementation of handwritten numeral recognition and a higher recognition rate and better performance. The main contents are summarized below.…”

Section: Discussionmentioning

confidence: 99%

The Study of Handwriting Recognition Algorithms Based on Neural Networks

Finkelstein¹,

Kuncan²

2021

IJHIT

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Handwriting Identifies basic graph-like problems and has a high real-world value in areas such as cloud accounting, finance, and postal administration. Due to the unrestricted problem of handwritten numbers when writing, it is relatively difficult to achieve rapid and effective recognition. With the emergence of deep learning-related algorithms and the rapid development of computer hardware technology, image classification methods based on Convolutional Neural Network (CNN) have gradually become a research hotspot. Because the convolutional network has a strong letter numbering ability and network generalization ability, the recognition rate can often exceed the traditional graph classing method. Therefore, the study of hand-written word recognition should be implemented using CNN through the network. Handwriting Word Recognition is the key technique for self-identification. Therefore, summarizing and analyzing the existing handwritten digit recognition algorithms, two handwritten digit recognition algorithms based on Convolutional Neural Network (CNN) are proposed. To improve the recognition performance of the CNN model, this article proposes a handwriting recognition algorithm based on the change to CNN. To extract the image feature information more fully, this paper proposes a handwriting recognition algorithm based on feature fusion and SVM. First, using the modified CNN model and the Gabor filter that introduces curvature systems, extract the CNN and Gabor characteristics of the character image; Second, the characteristics of its progress are fused to obtain more effective new features; Finally, the fusion feature is entered into the SVM classifier into the line number of words to recognize. The results of the experiment show that the algorithm can effectively improve the recognition effect of handwritten words

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“…CNN or ConvNet is a deep learning algorithm consisting of a number of layers. CNN has achieved an excellent performance in the various fields particularly in medical applications [26], [27]. The CNN has been reduced using sharing biases and weights comparing to other neural network approaches.…”

Section: Convolution Neural Network (Cnn)mentioning

confidence: 99%

Leukocytes identification using augmentation and transfer learning based convolution neural network

2022

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Most haematological diseases can be diagnosed using the morphological analysis of the microscopic blood image. The basic routine of the morphological analysis can be performed using the microscopic device which requires the skills and experiences of the haematologists. An inexperienced haematologist can lead to critical human errors. Therefore, this paper aims to propose an automated classification system used to classify different types of leukocytes based on the convolution neural network (CNN) algorithm. CNN has achieved robust performance in various fields especially in medical applications. A dataset of microscopic blood cells images of the conforming tags (basophil, eosinophil, erythroblast, lymphocyte, monocyte, neutrophil, and platelet) was used to train and test the proposed algorithm. The augmentation and deep transfer approaches were used to improve and enhance the performance of the CNN algorithm. The overall accuracy of the proposed classifier was 98% with Visual Geometry Group-19 (VGG-19). The obtained accuracy was higher than the state-of-art algorithms. To conclude that using the augmentation and deep transfer approaches with VGG-19 can obtain better classification results.

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Hierarchical Convolutional Neural Network for Handwritten Digits Recognition

Cited by 26 publications

References 17 publications

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

The Study of Handwriting Recognition Algorithms Based on Neural Networks

Leukocytes identification using augmentation and transfer learning based convolution neural network

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