An approach to empirical Optical Character recognition paradigm using Multi-Layer Perceptorn Neural Network (original) (raw)
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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.
2012 IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom), 2012
The studies of Optical Character Recognition (OCR) are being developed since it still needs a performance improvement. The previous study of alphanumeric character recognition had been conducted by Blumenstein and Liu using Modified Direction Feature (MDF) and Multi Layer Perceptrons (MLP) network. The study reaches the accuracy rate of 70.22% for lowercase characters and 80.83% for uppercase characters. In this study the OCR system is proposed to improve the existing performance and have a capability to recognize all case-sensitive alphanumeric characters simultaneously. One of the problems is that there are several characters having similarities in gesture and shape, so that the classifier of the OCR system encounters many ambiguities when classifying some particular characters, especially when recognizing all case-sensitive alphanumeric characters. To overcome those problems, this study proposes a technique of grouping. All character classes are clustered into some groups using Fuzzy C-Means (FCM) clustering method. The Nested MLP is the novelty of the previous method that is implemented in this study. This is a kind of multi-level MLP network that classifies the problem domain hierarchically. The first level classifies the character into the designated group and the second level continues the classification into the recognized character class. The OCR system using the methods to recognize all case-sensitive alphanumeric characters yields an accuracy rate of 84.38% for the uppercases, 76.43% for the lowercases, and 78.92% for the digits respectively. Any misclassified characters are mostly happened in distinguishing several uppercase and lowercase characters having similarities in gestures and shapes.
Genetic Algorithm and Neural Network for Optical Character Recognition
2015
Computer system has been able to recognize writing as human brain does. The method mostly used for character recognition is the backpropagation network. Backpropagation network has been known for its accuracy because it allows itself to learn and improving itself thus it can achieve higher accuracy. On the other hand, backpropagation was less to be used because of its time length needed to train the network to achieve the best result possible. In this study, backpropagation network algorithm is combined with genetic algorithm to achieve both accuracy and training swiftness for recognizing alphabets. Genetic algorithm is used to define the best initial values for the network’s architecture and synapses ’ weight thus within a shorter period of time, the network could achieve the best accuracy. The optimized backpropagation network has better accuracy and less training time than the standard backpropagation network. The accuracy in recognizing character differ by 10, 77%, with a succes...
IRJET- Optical Character Recognition using Artificial Neural Networks
IRJET, 2020
With no doubt keyboarding the most time consuming and labor-intensive operation is the most familiar method of data input into the computer. Optical Character Recognition is that the machine carbon of human reading and has been the subject of intensive research for three decades. OCR can be described as Mechanical or Electronic conversion of scanned images where images are often handwritten, typewritten or printed text. It is a way of digitizing printed texts in order that they will be electronically searched and utilized in machine processes. It converts the images into machine-encoded text that can be utilized in machine translation, text-to-speech and text mining. This paper presents a simple, efficient, and low-cost approach to construct OCR for reading any document that has fix font size and style or handwritten style. To achieve effectiveness and less computational cost, OCR in this paper uses database to recognize English characters which makes this OCR very simple to manage.
2011
The recognition of optical characters is known to be one of the earliest applications of Artificial Neural Networks, which partially emulate human thinking in the domain of Artificial Intelligence. The current paper focuses on the use of neural network in order to mitigate the problems of digital handwriting recognition by using Self-Organizing Map model for fast processing and less processing power consumption keeping its deployment on PDA in mind. The document is expected to serve as a resource for learners and amateur investigators in pattern recognition, neural networking and related disciplines. Technology Used is C#, .Net 3.5 Framework and Heaton Research Neural Network API has been used exhaustively for deploying the calibrated Self-Organising Model (SOM) approach. Characters are input when a user draws on a high-resolution box. Unfortunately, this resolution is too high to be directly presented to the neural network. To resolve this problem, we use the techniques of cropping...