Offline Character Recognition System Using Artificial Neural Network

Vasudeva, Nisha; Parashar, Hem Jyotsana; Singh, Vijendra

doi:10.7763/ijmlc.2012.v2.165

Cited by 14 publications

(6 citation statements)

References 14 publications

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“…N Vasudeva et al suggested a method with more appropriate results in comparision of matching with normalized wordimage [40]. Preprocessing focuses on deduction of the noise resulting due to the scanner quality used for capturing the image.…”

Section: Basic Stages In Character Recognitionmentioning

confidence: 99%

Effective Thinning Algorithm for Recognition of Hand Written Devnagri Compound Characters Using Neural Network

Yawalkar¹,

Kharat²

2018

International Journal of Applied Engineering Research

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Handwriting always works as a primary tool in the world of communication. Various techniques have been and are developed in order to document the handwritten text. There are certain exceptional techniques that describe the environment of handwritten scripts and further convert it into electronic data by implementing various algorithms. Devnagri is widely used for most popular and commonly used languages like Marathi and Hindi We have proposed a neural network based hand written Devnagri compound character recognition system using a novel rule-based thinning algorithm that improves the overall performance of the system. The distinctive feature of our thinning algorithm is that it is a rotation invariant method which thins characters to their central lines, helping in preserving shape of the character. The system applies different rules concurrently to each pixel in the image which results into symmetrical thinning and improvement in the overall speed of the system. Results obtained for the system prove that the system is efficient enough to preserve the topology of the compound characters written in Devnagari that further helps in improving the accuracy of the recognition system.

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Section: Basic Stages In Character Recognitionmentioning

confidence: 99%

Effective Thinning Algorithm for Recognition of Hand Written Devnagri Compound Characters Using Neural Network

Yawalkar¹,

Kharat²

2018

International Journal of Applied Engineering Research

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“…Nisha Vasudeva et al [7] briefly discuss the back propagation with feature extraction for character recognition. Authors compare different font's accuracy for different hidden nodes with a single hidden layer.…”

Section: Fazlul Kader and Kaushik Debmentioning

confidence: 99%

“…The number of iterations in back propagation is called epochs used in Back Propagation to solve the weight matrix feasible for all input matrix. The comparison of epochs and accuracy is given in below figure (from [7]):…”

Section: No Of Epochs Used To Calculate the Weight Matrixmentioning

confidence: 99%

Review of Text Recognition Works

Agarwal¹,

Kaushik²

2016

IJCA

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Character recognition is an important problem in Pattern Classification. It is difficult to recognize the character in images or pdf or scanned text or handwritten characters scan. There are various ways to recognize text which comprises of Neural Network, Genetic Algorithm, Soft computing and fuzzy logic. In this paper, we compare the various ways already explored by researchers. We have tried to compare the Artificial Neural Network and Genetic Algorithm for the character recognition. The character image is initially segmented into pixel array and then normalized and skew removed with feature extraction to form an input to the neural network or the genetic algorithm. We work with two set of data, training data and test data and the aim is to recognize character correctly in test data.

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“…They have used the Back propagation Neural Network for efficient recognition where the errors were corrected through back propagation and rectified neuron values were transmitted by feed-forward method in the neural network of multiple layers. [4] Kauleshwar Prasad, et.al, focused on recognition of English alphabet in a given scanned text document with the help of Neural Networks. Using Matlab Neural Network toolbox, also they have attempted to recognize handwritten characters by projecting them on different sized grids.…”

Section: Related Workmentioning

confidence: 99%

Hand Motion Tracking for Alphabet Recognition using ANN

Udhan¹,

Futane²,

Dharaskar³

2013

IJCA

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With the Objective of making communication process easy between handicap people and computer this paper describes a very basic method for recognizing the alphabets from hand motion trajectory. This method uses Artificial Neural Network (ANN) for alphabet recognition from gesture path (motion trajectory). This is done in three main stages; preprocessing on video input, feature extraction and classification. In first stage, preprocessing, sticker of particular colour placed on users hand is detected using color information. After detection of required colour to be traced, its motion trajectory which is also called as gesture path will be determined by tracking the sticker placed on hand. In the second stage, features are extracted from hand motion track which can be further used for training & testing ANN. In the final stage alphabet is recognized by using extracted features of gesture path.

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Offline Character Recognition System Using Artificial Neural Network

Cited by 14 publications

References 14 publications

Effective Thinning Algorithm for Recognition of Hand Written Devnagri Compound Characters Using Neural Network

Effective Thinning Algorithm for Recognition of Hand Written Devnagri Compound Characters Using Neural Network

Review of Text Recognition Works

Hand Motion Tracking for Alphabet Recognition using ANN

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