ISSN 2394-5125
 

Research Article 


HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare.

Abstract
Artificial neural networks tools are most widely used in machine learning. It is encouraged by the way the human brain works. As human has neuron structure through which the information of sensation is transformed from one neuron to another, in the aspect of neural networks, there is any number of input layers and output layers, along with one or more hidden layers in between. Artificial neural networks are designed to solve a complex problem like pattern recognition which is complex to recognize by a human. Due to the new technique called “backpropagation” that will be introduced, the neural networks will be a significant part of Artificial Intelligence. Handwritten character recognition (HCR) is a challenging field to study, as numerous variations are found in the writing style of humans. In this paper, we come up with a new approach to recognize handwritten characters for the English language using a Fuzzy min-max neural network (FMMNN). The suggested approach in this paper can improve the recognition rate of character.

Key words: Pattern Classification, Neural Network, hyperbox, hyperboxes, NN, membership function, FMN


 
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How to Cite this Article
Pubmed Style

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare. HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK . JCR. 2020; 7(19): 1041-1049. doi:10.31838/jcr.07.19.129


Web Style

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare. HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK . http://www.jcreview.com/?mno=104045 [Access: September 15, 2020]. doi:10.31838/jcr.07.19.129


AMA (American Medical Association) Style

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare. HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK . JCR. 2020; 7(19): 1041-1049. doi:10.31838/jcr.07.19.129



Vancouver/ICMJE Style

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare. HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK . JCR. (2020), [cited September 15, 2020]; 7(19): 1041-1049. doi:10.31838/jcr.07.19.129



Harvard Style

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare (2020) HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK . JCR, 7 (19), 1041-1049. doi:10.31838/jcr.07.19.129



Turabian Style

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare. 2020. HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK . Journal of Critical Reviews, 7 (19), 1041-1049. doi:10.31838/jcr.07.19.129



Chicago Style

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare. "HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK ." Journal of Critical Reviews 7 (2020), 1041-1049. doi:10.31838/jcr.07.19.129



MLA (The Modern Language Association) Style

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare. "HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK ." Journal of Critical Reviews 7.19 (2020), 1041-1049. Print. doi:10.31838/jcr.07.19.129



APA (American Psychological Association) Style

Prof. Kapil Tajane, Devesh Dhanrale, Sainath Utturkar, Atul Wagh,Atharav Waghmare (2020) HANDWRITTEN TEXT RECOGNITION USING FUZZY MIN-MAX NEURAL NETWORK . Journal of Critical Reviews, 7 (19), 1041-1049. doi:10.31838/jcr.07.19.129