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

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.

scholarly journals Classifying Handwritten Digit Recognition Using CNN and PSO

2019 ◽  
Vol 8 (2) ◽  
pp. 5983-5987
Keyword(s):  
Neural Network ◽  
Computation Time ◽  
Handwritten Digit Recognition ◽  
Swarm Optimization ◽  
Digit Recognition ◽  
Machine Learning Methods ◽  
Digital Database ◽  
Complexity Problem ◽  
Handwritten Digit ◽  
Normal Human

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.

Handwritten digit recognition using neural networks and dynamic zoning with stroke-based descriptors

2017 ◽  
Vol 25 (6) ◽  
pp. 979-990
Author(s):  
David Álvarez-León ◽  
Ramón-Ángel Fernández-Díaz ◽  
Lidia Sánchez-Gonzalez ◽  
José-Manuel Alija-Pérez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Process ◽  
Multilayer Perceptron ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Dynamic Zoning

Abstract This article presents an Off-line handwritten digit recognition approach based on neural networks. We define a numeric character as a composition of vertical and horizontal strokes. After the preprocessing, we use dynamic zoning to retrieve the positions where vertical strokes – the main strokes — are joined to horizontal strokes. These features are recorded into a representative string and verified using a custom matching pattern. Finally, a multilayer perceptron neural network is fed with the previous data to raise the learning process. The results gathered from the experiments performed on the well-known MNIST handwritten database are compared against other proposals providing promising results.

When Massive GPU Parallelism Ain’t Enough: A Novel Hardware Architecture of 2D-LSTM Neural Network

2022 ◽  
Vol 15 (1) ◽  
pp. 1-35
Author(s):  
Vladimir Rybalkin ◽  
Jonas Ney ◽  
Menbere Kina Tekleyohannes ◽  
Norbert Wehn
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Efficiency ◽  
Semantic Segmentation ◽  
Hardware Architecture ◽  
Handwritten Digit Recognition ◽  
Content Type ◽  
One Dimensional ◽  
Digit Recognition ◽  
Handwritten Digit

Multidimensional Long Short-Term Memory (MD-LSTM) neural network is an extension of one-dimensional LSTM for data with more than one dimension. MD-LSTM achieves state-of-the-art results in various applications, including handwritten text recognition, medical imaging, and many more. However, its implementation suffers from the inherently sequential execution that tremendously slows down both training and inference compared to other neural networks. The main goal of the current research is to provide acceleration for inference of MD-LSTM. We advocate that Field-Programmable Gate Array (FPGA) is an alternative platform for deep learning that can offer a solution when the massive parallelism of GPUs does not provide the necessary performance required by the application. In this article, we present the first hardware architecture for MD-LSTM. We conduct a systematic exploration to analyze a tradeoff between precision and accuracy. We use a challenging dataset for semantic segmentation, namely historical document image binarization from the DIBCO 2017 contest and a well-known MNIST dataset for handwritten digit recognition. Based on our new architecture, we implement FPGA-based accelerators that outperform Nvidia Geforce RTX 2080 Ti with respect to throughput by up to 9.9 and Nvidia Jetson AGX Xavier with respect to energy efficiency by up to 48 . Our accelerators achieve higher throughput, energy efficiency, and resource efficiency than FPGA-based implementations of convolutional neural networks (CNNs) for semantic segmentation tasks. For the handwritten digit recognition task, our FPGA implementations provide higher accuracy and can be considered as a solution when accuracy is a priority. Furthermore, they outperform earlier FPGA implementations of one-dimensional LSTMs with respect to throughput, energy efficiency, and resource efficiency.

scholarly journals Handwritten Digit Recognition using Image Preprocessing and CNN

2020 ◽  
pp. 896-902
Author(s):  
Anshul Dubey ◽  
Ashley Lazarus ◽  
Dharmendra Mangal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Accuracy Rate ◽  
Handwritten Digit Recognition ◽  
Image Preprocessing ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Multilayer Architecture ◽  
Recognition Systems ◽  
Zip Code

Handwritten digit recognition, is a technique of identifying and enlisting the recognized digit, that uses neural networks, deep learning and machine learning. The applications and demand of handwritten digit recognition systems such as zip code recognition, car number plate recognition, robotics, banks, mobile applications and numerous more, are soaring every day. It can be done through numerous approaches, but convolutional neural network is considered one of the best methods. The special neural network uses multilayer architecture for identification and classification. Although the accuracy factor can be increased, based on image preprocessing, in this paper we discuss how the accuracy of the system can be increased for better handwritten digit recognition, using convolutional neural networks, image preprocessing; binarization, resizing, rotation. The accuracy rate obtained is 99.33%.

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

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.

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

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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