RECOGNITION OF HANDWRITTEN DIGITS USING RBF NEURAL NETWORK (original) (raw)
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Identification of handwritten digits is one of the major areas of research in the field of character recognition. Artificial Neural Networks helps in computer vision that deals with how a computer could achieve high-level of understanding from digital images or videos. Thus, neural networks prove to be a boon in recognizing handwritten digits that are scanned as images. However, this paper aims at studying the working of specifically three neural networks-Multi-Layered Perceptron (MLP), Radial Basis Function (RBF) and Convolutional Neural Network (CNN). In order to focus majorly on the implementation of these three neural networks rather than the complexity of the dataset being used, we have used MNIST (Modified National Institute of Standard and Technology) dataset from keras library. The MNIST dataset contains 70,000 black and white images of handwritten English digits (60,000 training images and 10,000 testing images). In our study of the above three mentioned neural networks, we have used relu as activation function in the hidden layers and softmax as activation function in the final layer of neural network, Adam as an optimizer and cross-entropy as loss function. We have observed that all three networks give accuracy above 95%, however, the major difference is in its training time and error rate.
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International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), 2019
The importance of character recognition cannot be over emphasized. It finds applications in many automated system. In most cases these applications require high precision (e.g. automatic grading system, document digitization, license plate recognition systems, e.t.c) as well as low resource overhead. However, these are conflicting requirements, because the more the precision required, the more computation needed hence the more increase in resource overhead. In the research, two classification algorithms in Artificial Neural Networks (ANN): Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were applied to handwritten digit recognition and their performance is investigated. The duo were compared in terms of resources requirement for training and accuracy. It is found that MLP-NN is much faster to train (5.5min) compared to RBF (50.0min). However, during testing it is found that both have an accuracy of ≈ 95%.
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An off-line Nepali handwritten character recognition, based on the neural networks, is described in this paper. A good set of spatial features are extracted from character images. Accuracy and efficiency of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) classifiers are analyzed. Recognition systems are tested with three datasets for Nepali handwritten numerals, vowels and consonants. The strength of this research is the efficient feature extraction and the comprehensive recognition techniques, due to which, the recognition accuracy of 94.44% is obtained for numeral dataset, 86.04% is obtained for vowel dataset and 80.25% is obtained for consonant dataset. In all cases, RBF based recognition system outperforms MLP based recognition system but RBF based recognition system takes little more time while training.
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The purpose of this study is to evaluate the performance analysis of multilayer feed forward neural networks trained with back propagation algorithm & descent gradient Radial basis function network for the pattern classification of hand written curve script. This analysis has been done for handwritten text of three letters and for the individual English vowels. This analysis in the performance has been evaluated for the five different samples of handwritten English vowels and handwritten text of the three letters. Evaluation process is executed upon raw data in binary form and data based on extracted features (tangent values & density value) for each word & vowels. These characters are presented to the neural network for the training. Adjusting the connection strength and network parameters perform the training process in the neural network. The results of 3600 experiments indicate that the feed forward MLP performs accurately and exhaustively with imposed DG-RBF method. Key wordsPa...