A Mlp-Based Digit And Uppercase Characters Recognition System (original) (raw)
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2009
The purpose of this study is to analyze the performance of Back propagation algorithm with changing training patterns and the second momentum term in feed forward neural networks. This analysis is conducted on 250 different words of three small letters from the English alphabet. These words are presented to two vertical segmentation programs which are designed in MATLAB and based on portions (1/2 and 2/3) of average height of words, for segmentation into characters. These characters are clubbed together after binarization to form training patterns for neural network. Network was trained by adjusting the connection strengths on each iteration by introducing the second momentum term. This term alters the process of connection strength fast and efficiently. The conjugate gradient descent of each presented training pattern was found to identify the error minima for each training pattern. The network was trained to learn its behavior by presenting each one of the 5 samples (final input s...
Handwritten Digit Recognition System
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
Recently, handwritten digit recognition has become impressively significant with the escalation of the Artificial Neural Networks (ANN). Apart from this, deep learning has brought a major turnaround in machine learning, which was the main reason it attracted many researchers. We can use it in many applications. The main aim of this article is to use the neural network approach for recognizing handwritten digits. The Convolution Neural Network has become the center of all deep learning strategies. Optical character recognition (OCR) is a part of image processing that leads to excerpting text from images. Recognizing handwritten digits is part of OCR. Recognizing the numbers is an important and remarkable subject. In this way, since the handwritten digits are not of same size, thickness, position, various difficulties are faced in determining the problem of recognizing handwritten digits. The unlikeness and structure of the compositional styles of many entities further influences the example and presence of the numbers. This is the strategy for perceiving and organizing the written characters. Its applications are such as programmed bank checks, health, post offices, for education, etc. In this article, to evaluate CNN's performance, we used the MNIST dataset, which contains 60,000 images of handwritten digits. Achieves 98.85% accuracy for handwritten digit. And where 10% of the total images were used to test the data set.
Character Recognition Technique using Neural Network
2013
Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. The preprocessing of characters comprises bounding of characters for translation invariance and normalization of characters for size invariance. Now, the rapidly growing computational power enables the implementation of the present CR methodologies and also creates an increasing demand on many emerging application domains, which require more advanced methodologies. In this paper an attempt is made to develop neural network strategies for the isolated. Handwritten English characters (A to Z. a to z). The preprocessing of characters comprises bounding of characters for translation invariance and normalization of characters for size invariance. First, an overview of CR systems and their evolution over time is presented. Then, the available CR techniques with their superiorities and weaknesses are reviewed. Finally, the current...
Written Character Recognition using Neural Network Architecture
2014
Objective of this paper is to recognize character in this characters in a given scanned documents and study the effects of changing the Models of ANN. The approach has been found to be very suitable for handwritten character recognition as it provides fast feature extraction and classification.Character Recognition has been an active area of research in the past due to its diverse applications it continues to be a challenging research topic.HWR is the ability of a computer to receive and interpret intelligible written input from sources such as paper documents, photographs, touch-screens and other devices. Handwriting recognition principally entails optical character recognition. However, a complete handwriting recognition system also handles formatting, performs correct segmentation into characters and finds the most plausible words. The paper describes the behaviors of different Models of Neural Network used in OCR.OCR is widespread use of Neural Networks. We have used different M...
Machine recognition of Hand written Characters using neural networks
2011
Even today in Twenty First Century Handwritten communication has its own stand and most of the times, in daily life it is globally using as means of communication and recording the information like to be shared with others. Challenges in handwritten characters recognition wholly lie in the variation and distortion of handwritten characters, since different people may use different style of handwriting, and direction to draw the same shape of the characters of their known script. This paper demonstrates the nature of handwritten characters, conversion of handwritten data into electronic data, and the neural network approach to make machine capable of recognizing hand written characters.
HANDWRITTEN CHARACTER RECOGNITION USING FEED-FORWARD NEURAL NETWORK MODELS
Handwritten character recognition has been vigorous and tough task in the field of pattern recognition. Considering its application to various fields, a lot of work is done and is being continuing to improve the results through various methods. In this paper we have proposed a system for individual handwritten character recognition using multilayer feed-forward neural networks. For the experimental purpose we have taken 15 samples of lower & upper case handwritten English alphabets in scanned image format i.e. 780 different handwritten character samples. There are two methods of feature extraction are used to construct the pattern vectors for training set. This training set is presented to the six different feed-forward neural networks namely newff, newfit, newpr, newgrnn, newrb and newrbe. The test pattern set is used to evaluate the performance of these neural networks models. The results are compared to find the accuracy in recognition of the respective models. The number of hidden layer, number of neurons in hidden layer, validation checks and gradient factors of the neural networks models are taken into consideration during the training.
Handwritten Text Recognition using Neural Network
2017
Handwritten Text Recognition (HTR) is very challenging and subject of much attention in the field of recognition. This is due to the fact that writing styles of people vary to a great extent and it becomes difficult for the computer to recognize the handwritten characters. Various techniques are proposed in the literature including restrictions like specific writing styles-uppercase, lowercase or numeral characters. A more difficult problem is therecognition of characters when the writing style is not known a priori. The paper discusses handwritten English character recognition system using neural network. Simulation results include using neural based recognition for combined text comprising of capitalalphabets, small alphabets and numerals. The results show that neural network based method improves the text recognition in terms of accuracy.
Handwritten Digits Recognition Using a Multilayer Feed-Forward Backpropagation Neural Network
Nowadays, many researchers are trying to build a program that can recognize handwritten digits, so that can be used in many various field. For example, it can be applied to read checks in banks or numbers in car plates but it is a challenging problem that need an accurate classification program. Our mission is to let the computer receive and isolate the digits individually and then interpret every digit by using Artificial Neural Network(ANN) based on feedforward back propagation algorithm. Therefore, this approach needs a very precise data in order to train the artificial neural network to Increase the accuracy of neural network classification. This paper presents an approach of recognizing handwritten digit by using three stages which are image processing, neural network recognition which is the major mission, and displaying the result. We create a system for dealing with such challenging problem. The system started by acquiring an image containing digits, this image was digitized and modified, so it can be sent to the ANN in order to be recognized by using feed forward back propagation algorithm. The studies were conducted on the handwriting digits of 80 independent writers who contributed a total of 1593 isolated digits these digits divided into two data sets: Training 1115 digits, testing 478 digits. An overall accuracy meet using this system was 92% on the test data set used.