Survey On Optical Character Recognition Using Neural Network (original) (raw)
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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.
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...
In this paper we are represent the architecture of Optical Character Recognition that converting from visual character to the machine readable format. To present this architecture, several stages are associate like take the character input image, preprocessing the image, feature extraction of the image and at last take a decision by the artificial computational model same as biological neuron network. Decision making system by the Artificial Neural Network associated with two steps; first is adapted the artificial neural network throughout the Multi-Layer Perceptron learning algorithm and second is recognition or classification process for the character image to comprehensible for the machine in a way that what character is it. Our proposal architecture achieved 91.53% accuracy to recognize the isolated character image and 80.65% accuracy for the sentential case character image.
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.
Optical Character Recognition Using Neural Networks
2020
Optical character affirmation (OCR) is technique where images, composed archives or printed content is transformed into machine-coded content whether the substance might be from a photo of a report, a scene-photo and separated records. It is utilized as a sort of data entry from imprinted paper data records, mail, business cards, sales, printouts of composed data, or any suitable representation. It is a strategy for digitizing printed messages with the objective that they can be electronically changed.
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.