Genetic Algorithm and Neural Network for Optical Character Recognition (original) (raw)

A Modified Genetic Based Neural Network Model For Online Character Recognition

Character Recognition has become an intensive research areas during the last few decades because of its potential applications. However, most existing classifiers used in recognizing handwritten online characters suffer from poor feature selection and slow convergence which affect training time and recognition accuracy. This paper proposed a methodology that is based on extraction of structural features (invariant moment, stroke number and projection) and a statistical feature (zoning) from the characters. A genetic algorithm was modified through its fitness function and genetic operators to minimize the character recognition errors. The Modified Genetic Algorithm (MGA) was used to select optimized feature subset of the character to reduce the number of insignificant and redundant features. A genetic based neural network model was developed by integrating the MGA into an existing Modified Optical Backpropagation (MOBP) learning algorithm to train the network. Three classifiers (C1, C2 and C3) were then formulated from MGA-MOBP such that C1 classified without using MGA at classification level, C2 classified using MGA at classification level while C3 employed MGA at feature selection level and classified at classification level The developed C3 achieves a better performance of recognition accuracy and recognition time.

Survey On Optical Character Recognition Using Neural Network

In this paper, an Optical Character recognition system based on Artificial Neural Networks (ANNs). The ANN is trained using the Back Propagation algorithm. In the proposed system, each typed English letter is represented by binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input, are fed to an ANN. Afterwards, the Feed Forward Algorithm gives insight into the enter workings of a neural network followed by the Back Propagation Algorithm which compromises Training, Calculating Error, and Modifying Weights.

Optical Character Recognition with Neural Network

A neural network is defined a computing architecture that consist of massively parallel interconnection of simple neural process. Because of its parallel nature it can perform computation at a higher rate compared to the classical techniques. A neural network contains many nodes.OCR is the acronym for Optical Character Recognition. This technology allows a machine to automatically recognize characters through as optical mechanism.Character reorganization device is one of such smart devices that acquire partial human intelligence with the ability to capture and recognize various characters and digits. Character recognition techniques help in recognizing the characters written on paper documents and converting it in digital form. So Character recognition is gaining interest and importance in the modern world. While the area of character recognition is vast we focus on the fundamentals of character recognition, available techniques and emphasis on more recently used technique, neural networks. Recognizing characters, letters or digits for human beings is not a big task. It can even be done by small child, but doing the same with machine is a difficult task. Machine simulation of human functions has been a very challenging research area since the advent of digital computers. Character recognition techniques help in recognizing the characters written on paper documents and converting it in digital form. So Character recognition is gaining interest and importance in the modern world. The paper throws light on, one of the application of Neural Network (NN) i.e. Character Recognition.

Character Recognition Using BackPropagation Method

2015

This paper describe how handwritten English character Recognition (HCR) processed, trained and then recognized using Back propagation method. Size and fonts are different in training the data and testing the data. In the present paper, we have given a method to recognize a handwritten character using back propagation method. It is developed for isolated handwritten English Characters (A to Z). Preprocessing of Recognition is used binarization, thresolding and segmentation method. Image are first converted into gray scale then features are extracted which are in form of 0 and 1. 780 hand written characters are used in database for characters then test the data and find the Recognition accuracy.

Neural Network with Regression Algorithms for Optical Character Recognition

International Journal of Engineering & Technology

In today's automatic and robust modern world, possibilities of optical character recognition is endless. Previously OCR was used in postal service to read address from mail, car number plate tracking, automation of bank check transfer but today it has taken document management system to whole new level. Using OCR we can convert normal hardcopy document into Searchable text. We will use deep Neural network to train systems to recognise characters in a precise manner, basically we have proposed neural network model combined with machine learning technique like gradientDescent, regression, softmax normalization which will help to increase the efficiency of the OCR. Computer will able to recognise hand written digit. We will be using Google's advanced TensorFlow to create an OCR system which will be efficient and robust in action.