Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers

Siddique, Fathma; Sakib, Shadman; Siddique, Md. Abu Bakr

doi:10.20944/preprints201903.0039.v2

Cited by 17 publications

(16 citation statements)

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“…MNIST is a database of handwritten numbers widely used as a testbed for various deep learning applications. It has 70,000 images, of which 60,000 are training images and 10,000 are testing images [110]. Figure 10 shows sample images from the MNIST dataset.…”

Section: Image Classification Using the D-cnnmentioning

confidence: 99%

Deep CNN and Deep GAN in Computational Visual Perception‐Driven Image Analysis

et al. 2021

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Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides.

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Section: Image Classification Using the D-cnnmentioning

confidence: 99%

Deep CNN and Deep GAN in Computational Visual Perception‐Driven Image Analysis

et al. 2021

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“…They claimed that their proposed architecture is able to provide an improved accuracy of 98.85% within 8569 seconds. Another CNN model was presented for MNIST dataset in the same manner by authors in [16] in which CNN models consisted of seven layers. This model included one input and one output layer with five hidden layers in the middle.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient CNN Model for Automated Digital Handwritten Digit Classification

Biswas

Islam

2021

JISEBI

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Background: Handwriting recognition becomes an appreciable research area because of its important practical applications, but varieties of writing patterns make automatic classification a challenging task. Classifying handwritten digits with a higher accuracy is needed to improve the limitations from past research, which mostly used deep learning approaches.Objective: Two most noteworthy limitations are low accuracy and slow computational speed. The current study is to model a Convolutional Neural Network (CNN), which is simple yet more accurate in classifying English handwritten digits for different datasets. Novelty of this paper is to explore an efficient CNN architecture that can classify digits of different datasets accurately.Methods: The author proposed five different CNN architectures for training and validation tasks with two datasets. Dataset-1 consists of 12,000 MNIST data and Dataset-2 consists of 29,400-digit data of Kaggle. The proposed CNN models extract the features first and then performs the classification tasks. For the performance optimization, the models utilized stochastic gradient descent with momentum optimizer.Results: Among the five models, one was found to be the best performer, with 99.53% and 98.93% of validation accuracy for Dataset-1 and Dataset-2 respectively. Compared to Adam and RMSProp optimizers, stochastic gradient descent with momentum yielded the highest accuracy.Conclusion: The proposed best CNN model has the simplest architecture. It provides a higher accuracy for different datasets and takes less computational time. The validation accuracy of the proposed model is also higher than those of in past works.

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“…Their method for the mixed-script numerals (considering 20-class classification problem) yielded an accuracy of 98.44% on both the ISI handwritten Bangla and MNIST (LeCun, 1998) numeral databases. The work done by Siddique et al (2019) analysed the impact of variations of hidden layers, batch sizes and epochs of CNNs for numerals written in Latin script. The analysis showed the maximum accuracy of 99.21% (considering four hidden layers, batch size of 100 and 14 epochs) on the MNIST dataset (LeCun, 1998).…”

Section: Existing Workmentioning

confidence: 99%

A new feature extraction approach for script invariant handwritten numeral recognition

et al. 2021

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Handwritten numeral recognition is a challenging research problem because of the enormous varieties of styles in which human beings write the numerals. Several researchers have tried to find solutions to this problem with exceptional recognition accuracies. However, most of these solutions have been dedicated to single script numerals. Such methods are inappropriate for multi-lingual nations such as India where a large number of scripts are used. Keeping this issue in mind, a new feature descriptor named symbolization of binary images (SBI) is introduced here for the recognition of handwritten numerals of different scripts. Effectiveness of SBI is supported with experiments showing its script-invariant nature. Classification of numerals using a multiclass support vector machine (SVM) classifier yields the recognition accuracies of 98.18, 96.22, 96.52, and 95.53% on datasets of numerals written in four popular scripts of the world: Arabic, Bangla, Devanagari, and Latin, respectively. This scheme has also been extended to the situation when the script used is not known a priori or the numerals written in a document belong to pairs of mixed scripts of {Arabic, Devanagari, Bangla} with Latin producing recognition rates of 92.97, 91.25, and 91.67%, respectively. When all four scripts are mixed, the recognition rate is still 90.98% overall. Encouraging outcomes suggest that the proposed SBI feature descriptor can recognize numerals invariant of the script class.

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Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers

Cited by 17 publications

References 0 publications

Deep CNN and Deep GAN in Computational Visual Perception‐Driven Image Analysis

Deep CNN and Deep GAN in Computational Visual Perception‐Driven Image Analysis

An Efficient CNN Model for Automated Digital Handwritten Digit Classification

A new feature extraction approach for script invariant handwritten numeral recognition

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