2012
DOI: 10.5120/4986-7250
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Recognition of Isolated Handwritten Oriya Numerals using Hopfield Neural Network

Abstract: Designing an automatic pattern recognition system is a challenging task. However, despite the design challenges, its enormous application potentials have attracted the attention of researchers and developers over the last four to five decades. Design of recognition systems for handwritten character applications has been a subject of intensive research, and the search is still on for a robust technique capable of dealing with natural variations in handwritten characters. In this paper the performance of Hopfiel… Show more

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Cited by 25 publications
(5 citation statements)
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“…The authors of www.ijacsa.thesai.org [10] used zone centroid distance and standard deviation to extract features and got 94% accuracy by back propagation NN with a genetic algorithm approach. [11], [12], [13]- [15], [16], [17] [18] had contributed their work on handwritten Odia handwritten numeral recognition (Odia OHNR). The same BESAC features are used for numeral classification, on the IITBBS numeral dataset [9].…”
Section: Related Workmentioning
confidence: 99%
“…The authors of www.ijacsa.thesai.org [10] used zone centroid distance and standard deviation to extract features and got 94% accuracy by back propagation NN with a genetic algorithm approach. [11], [12], [13]- [15], [16], [17] [18] had contributed their work on handwritten Odia handwritten numeral recognition (Odia OHNR). The same BESAC features are used for numeral classification, on the IITBBS numeral dataset [9].…”
Section: Related Workmentioning
confidence: 99%
“…Bhowmik et al [10] suggested a system for Odia handwritten digits with approximately 93% accuracy by using hidden markov as a classifier. Sarangi et al [11] proposed a classifier by using Hopfield neural network for Odia numerals. Panda et al [12] proposed a single layer perception for Odia handwritten digits recognition by using gradient and curvature feature extraction methods with an accuracy of 85%.…”
Section: Related Workmentioning
confidence: 99%
“…Oriya script was recognized using curvature feature, chain code based on SVM accuracy result is 94% [10].The author proposed a model to segment the Oriya handwritten character using water reservoir concept achieved 96.7% [11]. The author recognizes the handwritten character of Oriya numerals using Hopfield neural networks (HNN)of image cropping, resizing, digitalization of different data sets of this script recognition accuracy is 95.4% [12]. The author proposed a model to recognize the Java Character using Hopfield network algorithm achieved accuracy is 88% [13].…”
Section: Odia Numeralsmentioning
confidence: 99%
“…Represent the network state containing N number of neurons by its vector form as, 12 , ,.... ..... In the network each and every neuron interconnected with all other neurons to form fully-connected network.…”
Section: A Discrete Hopfield Networkmentioning
confidence: 99%