Classifying Handwritten Digit Recognition Using CNN and PSO

A normal human can easily recognize any written or typed or scanned text, numbers, etc., but when it comes to a machine, it is difficult to find out what exactly that given text or numbers. It will be difficult to recognize a handwritten digit for a machine. Many machine learning methods were used to fix the handwritten digit recognition issue. It is growing in more convoluted domains, so its training complexity is also increasing. To overcome this complexity problem, many algorithms have been implemented. In this paper, the Convolutional Neural Network (CNN) and Particle Swarm Optimization (PSO), those two approaches do use for recognition of the isolated handwritten digit. Customized PSO is used to reduce the overall computation time of the proposed system. The customized PSO used with CNN, to decreases the required number of epochs for training. It is used to identify digits in the MNIST handwritten digital database to predict the number. The system has achieved an average of 94.90% accuracy.

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Comparison of the efficiency of neural network algorithms in recognition and classification problems

E3S Web of Conferences ◽

10.1051/e3sconf/202022401025 ◽

2020 ◽

Vol 224 ◽

pp. 01025

Author(s):

Alexey Beskopylny ◽

Alexandr Lyapin ◽

Nikita Beskopylny ◽

Elena Kadomtseva

Keyword(s):

Neural Network ◽

Neural Networks ◽

Computation Time ◽

Classification Problems ◽

Handwritten Digit Recognition ◽

Network Algorithms ◽

Digit Recognition ◽

Labour Intensity ◽

Handwritten Digit ◽

Accuracy Of Results

The article is devoted to the problem of comparing the effectiveness of feedforward (FF) and convolutional neural networks (CNN) algorithms in the problems of handwritten digit recognition and classification. In recent years, the attention of many researchers to the FF and CNN algorithms has given rise to many hybrid models focused on solving specific problems. At the same time, the efficiency of each algorithm in terms of accuracy and labour intensity remains unclear. It is shown that in classical problems, FFs can have advantages over CNN in terms of labour intensity with the same accuracy of results. Using the handwritten digits data from the MNIST database as an example, it is shown that FF algorithms provide greater accuracy and require less computation time than CNN.

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An Innovative Method to Analyse the Prediction Rate and Accuracy for Handwritten Digit Recognition with Convolutional Neural Network Over Connection Temporal Classification

Alinteri Journal of Agricultural Sciences ◽

10.47059/alinteri/v36i1/ajas21092 ◽

2021 ◽

Vol 36 (1) ◽

pp. 650-656

Author(s):

M. Pranathi Sai Prathyusha ◽

Dr.K. Malathi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Handwritten Digit Recognition ◽

Network Algorithms ◽

Digit Recognition ◽

Machine Learning Methods ◽

Innovative Method ◽

Prediction Rate ◽

Handwritten Digit ◽

Connectionist Temporal Classification

Aim: Recognizing the Handwritten Digits to find the best accuracy using Machine learning methods such as Connectionist Temporal Classification (CTC) and Convolutional Neural Network (CNN). Methods and Materials: Accuracy and loss are performed with the MNIST dataset from the Keras library. The two groups Connectionist Temporal classification (N=20) and Convolutional Neural Network algorithms (N=20). Results: A CNN is used for recognizing the innovative handwritten digits. The accuracy is analysed based on correctness of the exact digits of 92.67% where the CTC has the accuracy of 89.07%. The two algorithms CNN and CTC are statistically satisfied with the independent sample T-Test (=.001) value (p<0.05) with confidence level of 95%. Conclusion: Recognizing the handwritten digits significantly seems to be better in CNN than CTC.

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Handwritten digit recognition using trace neural network with EKF training algorithm

ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357) ◽

10.1109/icecs.1999.812281 ◽

2003 ◽

Author(s):

Kwok-Wo Wang ◽

Sheng-Jiang Chang ◽

Chi-Sing Leung

Keyword(s):

Neural Network ◽

Training Algorithm ◽

Handwritten Digit Recognition ◽

Digit Recognition ◽

Handwritten Digit

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Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/4/16 ◽

2020 ◽

Vol 17 (4) ◽

pp. 572-578

Author(s):

Mohammad Parseh ◽

Mohammad Rahmanimanesh ◽

Parviz Keshavarzi

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Support Vector ◽

Svm Classifier ◽

Handwritten Digit Recognition ◽

Digit Recognition ◽

Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

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