scholarly journals Handwritten Character Recognition Using Neural Network

2021 ◽  
pp. 203-207
Author(s):  
Monika ◽  
Monika Ingole ◽  
Khemutai Tighare
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Execution Time ◽  
Character Recognition ◽  
Recognition System ◽  
Handwritten Character Recognition ◽  
Person Recognition ◽  
Community Approach ◽  
Artificial Neural

In this paper, an enterprise is made to perceive manually written characters for English letters so as. The precept point of this mission is to plan a master framework for, "HCR(English) utilizing neural community". That could viably understand a particular individual-of-kind layout making use of the artificial neural community approach. The manually written man or woman acknowledgment trouble has grown to be the maximum famous trouble in ai. Handwritten man or woman acknowledgment has been a difficult space of exam, with the execution of gadgets getting to know we suggest a neural network-based methodology. The development is based totally on neural network, that is a subject of look at in artificial intelligence. Distinct strategies and methods are used to broaden a handwriting person recognition system. Acknowledgment, precision fee, execution, and execution time are massive versions on the way to be met through the technique being applied. The purpose is to illustrate the effectiveness of neural networks for handwriting character recognition.

scholarly journals Handwritten Character Recognition using Deep Learning and Neural Network

2021 ◽  
pp. 371-374
Author(s):  
Nikita Aware ◽  
Ashwini Bhagat ◽  
Komal Ghorpade ◽  
Komal Kerulkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Character Recognition ◽  
Recognition System ◽  
Handwritten Character Recognition ◽  
Query Image ◽  
Research Areas ◽  
Handwritten Character ◽  
Challenging Research ◽  
The Neural Networks

Handwritten character recognition is among the most challenging research areas in pattern recognition and image processing. With everything going digital, applications of handwritten character recognition are emerging in different offices, educational institutes, healthcare units, commercial units and banks etc., where the documents that are handwritten are dealt more frequently. Many researchers have worked with recognition of characters of different languages but there is comparatively less work carried for Devanagari Script. In past few years, however the work carried out in this direction is increasing to a great extent. Handwritten Devanagari Character Recognition is more challenging in comparison to the recognition of the Roman characters. The complexity is mostly due to the presence of a header line known as shirorekha that connects the Devanagari characters to form a word. The presence of this header line makes the segmentation process of characters more difficult. There is uniqueness to the handwriting styles of every individual which adds to the complexity. In this paper, a recognition system based on neural network has been proposed for Devanagari (Marathi) alphabets. Each of the characters that are extracted through query image is resized and is then passed to the neural networks for the process of recognition.

scholarly journals Development and training of neural networks for character recognition

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
R. D. Bukov ◽  
◽  
I. S. Shcherbyna ◽  
O. V. Nehodenko ◽  
Ye. S. Tykhonov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Character Recognition ◽  
Intelligent Systems ◽  
Recognition System ◽  
The Neural Network ◽  
Reference Images ◽  
Artificial Neural ◽  
Self Learning

This article discusses the problem of the application of neural networks for character recognition, as well as the problem of developing methods and algorithms for the synthesis of neural networks. To solve the problems of optimizing the character recognition system, highly intelligent systems based on artificial neural networks are often used. However, artificial neural networks are not a tool for solving problems of any type. They are unsuitable for tasks such as payroll, but they have an advantage for character recognition tasks that conventional personal computers do poorly or not at all. It has been proven that artificial neural networks can be used for predictive modeling, adaptive control and applications where they can be trained using a dataset. Experiential self-learning can occur in networks that can draw inferences from a complex and seemingly unrelated set of information. The application of neural networks for solving practical problems in the field of character recognition and their classification is shown. It has been established that images can denote objects of different nature: text symbols, images, sound samples. When training the network, various sample images are offered with an indication of which class they belong to. At the end of training the network, you can present previously unknown images and receive an answer from it about belonging to a certain class. The topology of such a network is characterized by the fact that the number of neurons in the output layer, as a rule, is equal to the number of conditioned classes. This establishes a correspondence between the output of the neural network and the class it represents. A method for training a neural network is proposed, according to which the person managing the network takes a direct part in training the network, it itself sets the reference images of all symbols, as well as distorted images of the standards (plagued copies).

scholarly journals HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS

2021 ◽  
Vol 10 (8) ◽  
pp. 41-45
Author(s):  
D.J. Samatha Naidu ◽  
T. Mahammad Rafi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Character Recognition ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Recognition System ◽  
Self Organizing Map ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
The Neural Network

Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters & special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks. In proposed system this project design a image segmentation based hand written character recognition system. The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition. The system easily identify or easily recognize text in English languages and letters, digits. By using Open cv for performing image processing and having tensor flow for training the neural network. To develop this concept proposing the innovative method for offline handwritten characters. detection using deep neural networks using python programming language.

scholarly journals An Automatic Logistics System using Artificial Intelligence

2019 ◽  
Vol 8 (2S4) ◽  
pp. 924-926
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Optimal Route ◽  
Logistics System ◽  
The Neural Network ◽  
Artificial Intelligence Technology ◽  
Artificial Neural ◽  
Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

scholarly journals The novel Artificial Neural Network assisted models: A review

2021 ◽  
Author(s):  
Bhanu Srivastav
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Web Of Science ◽  
The Other ◽  
The Novel ◽  
Neural Structure ◽  
Recent Advancement ◽  
Artificial Neural ◽  
Estimation Models

Neural networks are one of the methods of artificial intelligence. It is founded on an existingknowledge and capacity to learn by illustration of the biological nervous system. Neuralnetworks are used to solve problems that could not be modeled with conventional techniques.A neural structure can be learned, adapted, predicted, and graded. The potential of neuralnetwork parameters is very strong prediction. The findings are more reliable than standardmathematical estimation models. Therefore, it has been used in different fields.This research reviews the most recent advancement in utilizing the Artificial neural networks.The reviewed studies have been extracted from Web of Science maintained by ClarivateAnalytics in 2021. We find that among the other applications of ANN, the applications onCovid-19 are on the rise.

scholarly journals Sustainability through the Use of Modern Simulation Methods—Applied Artificial Intelligence

2019 ◽  
Vol 11 (8) ◽  
pp. 2384 ◽  
Author(s):  
Constantin Ilie ◽  
Catalin Ploae ◽  
Lucia Violeta Melnic ◽  
Mirela Rodica Cotrumba ◽  
Andrei Marian Gurau ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Simulation Methods ◽  
Cognitive Response ◽  
European Economic ◽  
Artificial Neural ◽  
Training Error

As the transformative power of AI crosses all economic and social sectors, the use of it as a modern technique for the simulation and/or forecast of various indicators must be viewed as a tool for sustainable development. The present paper reveals the results of research on modeling and simulating the influences of four economic indicators (the production in industry, the intramural research and development expenditure, the turnover and volume of sales and employment) on the evolution of European Economic Sentiment using artificial intelligence. The main goal of the research was to build, train and validate an artificial neural network that is able to forecast the following year’s value of economic sentiment using the present values of the other indicators. Research on predicting European Economic Sentiment Indicator (ESI) using artificial neural networks is a starting point, with work on this subject almost inexistent, the reason being mainly that ESI is a composite of five sectoral confidence indicators and is not thought to be an emotional response to the interaction of the entrepreneurial population with different economic indicators. The authors investigated, without involving a direct mathematical interaction among the indicators involved, predicting ESI based on a cognitive response. Considering the aim of the research, the method used was simulation with an artificial neural network and a feedforward network (structure 4-9-6-1) and a backward propagation instruction algorithm was built. The data used are euro area values (for 19 countries only—EA19) recorded between 1999 and 2016, with Eurostat as the European Commission’s statistical data website. To validate the results, the authors imposed the following targets: the result of the neural network training error is less than 5% and the prediction verification error is less than 10%. The research outcomes resulted in a training error (after 30,878 iterations) of less than 0.099% and a predictive check error of 2.02%, which resulted in the conclusion of accurate training and an efficient prediction. AI and artificial neural networks, are modeling and simulation methods that can yield results of nonlinear problems that cover, for example, human decisions based on human cognitive processes as a result of previous experiences. ANN copies the structure and functioning of the biological brain, having the advantage through learning and coaching processes (biological cognitive), to copy/predict the results of the thinking process and, thus, the process of choice by the biological brain. The importance of the present paper and its results stems from the authors’ desire to use and popularize modern methods of predicting the different macroeconomic indices that influence the behavior of entrepreneurs and therefore the decisions of these entrepreneurs based on cognitive response more than considering linear mathematical functions that cannot correctly understand and anticipate financial crises or economic convulsions. Using methods such as AI, we can anticipate micro- and macroeconomic developments, and therefore react in the direction of diminishing their negative effects for companies as well as the national economy or European economy.

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