{"id":638,"date":"2018-12-03T08:26:51","date_gmt":"2018-12-03T08:26:51","guid":{"rendered":"http:\/\/www.gyanvihar.org\/journals\/?p=638"},"modified":"2019-05-25T12:01:27","modified_gmt":"2019-05-25T12:01:27","slug":"evaluation-of-multi-hidden-layer-neurons-in-multiple-layers-bpnn-architecture","status":"publish","type":"post","link":"https:\/\/www.gyanvihar.org\/journals\/evaluation-of-multi-hidden-layer-neurons-in-multiple-layers-bpnn-architecture\/","title":{"rendered":"EVALUATION OF MULTI HIDDEN LAYER NEURONS IN MULTIPLE LAYERS BPNN ARCHITECTURE"},"content":{"rendered":"

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R<\/em><\/strong>ak<\/em><\/strong>esh Kumar Bhujade, Amitkant Pandit, Stuti Asthana<\/em><\/strong><\/span><\/p>\n

1,3<\/em><\/strong>Phd Scholar, Department of CSE, Suresh GyanVihar University, Jaipur- 302025, Rajasthan, India<\/em><\/strong><\/span><\/p>\n

2<\/em><\/strong>S<\/em><\/strong>ECE, Shri Mata Vaishno Devi University, Jammu, India Corresponding Author email: <\/em><\/strong>r<\/em><\/strong>a<\/em><\/strong>kesh.bhujade@gmail.com<\/em><\/strong><\/a><\/span><\/p>\n

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Abstract-<\/strong>Process of feature extraction and the extracted feature has to be classified and for the same Artificial Neural Network (ANN) has been used. There are numerous classifiers but BPNN is most preferable because it is easy to handle and less error prone and apart from that its accuracy is much higher compared to other classifier. The classification has beenanalyzed individually with the two extracted features and finally the features were cascaded to increase the accuracy.To recognize the Handwritten English Character using a multilayer perceptron with one hidden layer has been considered. The feature extracted from the handwritten character is Boundary tracing along with Fourier Descriptor. Character is identified by analyzing its shape and comparing its features that distinguishes each character. An analysis has been done to determine the number of hidden layer nodes to achieve high performance of backpropagation network in the recognition of handwritten English characters.<\/span><\/p>\n

K<\/strong>e<\/strong>y<\/strong>w<\/strong>o<\/strong>r<\/strong>d<\/strong>s: <\/strong>Back Propagation Neural Network (BPNN), Hidden layer, Perceptron, Character recognition.<\/span><\/p>\n

INTRODUCTION<\/span><\/p>\n

Neural Networks are recently being used in various kind of pattern recognition. Handwritings of the different human being are different; therefore it is very difficult to recognize the handwritten characters. The Handwritten Character recognition is an area of pattern recognition that has become the subject of research during the last several decades. Neural system is playing an important role in handwritten character appreciation. several information of character recognition in English have been published but still high recognition accuracy and minimum training time of handwritten English characters using neural network is an open problem. Consequently, it is a great important to develop an automatic handwritten character recognition system for English language [1]. In this work, efforts have been made to develop automatic handwritten character recognition system for English language with high recognition accuracy and minimum training and classification time. Experimental result shows that the approach used in this paper for English character recognition is giving high recognition accuracy and minimum training time.<\/span><\/p>\n

C<\/em>ha<\/em>r<\/em>a<\/em>cter Modeling<\/em><\/span><\/p>\n

As the English language consists of 26 characters (5 vowels, 21 consonants) and is written from left to right correctly A place of hand written English characters is shown in Fig. 1.<\/span><\/p>\n

Fig. 1A Set of Handwritten English Characters<\/span><\/p>\n

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S<\/em>canning and Skeletonization<\/em><\/span><\/p>\n

Handwritten characters are scanned and it has been converted into 1024 (32X32) binary pixels. The skeletonization process may be used to binary pixel image and the extra pixels which are not belonging to the backbone of the character has been deleted and the broad strokes has been reduced to thin lines [1]. Skeletonization is illustrated in Fig. 2.<\/span><\/p>\n

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Fig. 2 Skeletonization of an English Character<\/span><\/p>\n

N<\/em>ormalization<\/em><\/span><\/p>\n

There are lots of variations in handwritings of dissimilar people. consequently, after skeletonization procedure it normalization of characters is performed so that all characters could become in equal dimensions of environment. In this study, characters are normalized into 30X30 pixel character and move to the top left corner of pixel window.<\/span><\/p>\n

Bhujade et. al.<\/span>SGVU Int J Env Sci Technol,Vol1,Issue2,29-36<\/span><\/p>\n

Handwritten Characters –\u00a0\u00a0<\/span>Scanning of Characters –\u00a0<\/span>Skeletionization –\u00a0 \u00a0Normalization\u00a0 \u00a0Feature Extraction –<\/span>Neural Network Classification\u00a0 \u00a0 –\u00a0\u00a0<\/span>Output (Recognized Characters)<\/span><\/p>\n

Fig. 3: Block Diagram of the Recognition System<\/span><\/p>\n

C<\/em>ha<\/em>r<\/em>a<\/em>cter Recognition System<\/em><\/span><\/p>\n

Classification:- For classification or recognition<\/span><\/p>\n

The obstruct figure of Character recognition system is\u00a0<\/span>shown in following Fig 3.<\/span><\/p>\n

The process of handwritten English character recognition is as follows:<\/span><\/p>\n

Acquire the sample by scanning.<\/span><\/p>\n

Normalization \u00a0and\u00a0 \u00a0Skeletionization\u00a0 \u00a0process\u00a0\u00a0 \u00a0are\u00a0<\/span>execute.<\/span><\/p>\n