Based on discrete Hopfield neural network and wavelet transform character recognition

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

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Odia Characters and Numerals Recognition using Hopfield Neural Network Based on Zoning Feature

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3763.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 4928-4937 ◽

Cited By ~ 1

Keyword(s):

Neural Network ◽

Neural Networks ◽

Character Recognition ◽

Optical Character Recognition ◽

Hopfield Neural Network ◽

Hopfield Network ◽

Research Areas ◽

Different Types ◽

Classification Tasks ◽

Text Version

Odia character and digits recognition area are vital issues of these days in computer vision. In this paper a Hope field neural network design to solve the printed Odia character recognition has been discussed. Optical Character Recognition (OCR) is the principle of applying conversion of the pictures from handwritten, printed or typewritten to machine encoded text version. Artificial Neural Networks (ANNs) trained as a classifier and it had been trained, supported the rule of Hopfield Network by exploitation code designed within the MATLAB. Preprocessing of data (image acquisition, binarization, skeletonization, skew detection and correction, image cropping, resizing, implementation and digitalization) all these activities have been carried out using MATLAB. The OCR, designed a number of the thought accuses non-standard speech for different types of languages. Segmentation, feature extraction, classification tasks is the well-known techniques for reviewing of Odia characters and outlined with their weaknesses, relative strengths. It is expected that who are interested to figure within the field of recognition of Odia characters are described in this paper. Recognition of Odia printed characters, numerals, machine characters of research areas finds costly applications within the banks, industries, offices. In this proposed work we devolve an efficient and robust mechanism in which Odia characters are recognized by the Hopfield Neural Networks (HNN).

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Handwritten character recognition using Hopfield neural network

10.1117/12.152576 ◽

1993 ◽

Author(s):

C. Olson ◽

Robert Y. Li

Keyword(s):

Neural Network ◽

Character Recognition ◽

Hopfield Neural Network ◽

Handwritten Character Recognition ◽

Handwritten Character

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A Study of Associative Memories with Hopfield neural network Model for handwritten character recognition

SRMS Journal of Mathmetical Science ◽

10.29218/srmsjoms.v3i01.10876 ◽

2018 ◽

Vol 3 (01) ◽

Author(s):

Sandeep Kumar ◽

Manu Pratap Singh

Keyword(s):

Neural Network ◽

Associative Memory ◽

Network Model ◽

Neural Network Model ◽

Character Recognition ◽

Hopfield Neural Network ◽

Recurrent Network ◽

Associative Memories ◽

Bidirectional Associative Memory ◽

Content Addressable Memory

Neural network is the most important model which has been studied in past decades by several researchers. Hopfield model is one of the network model proposed by J.J. Hopfield that describes the organization of neurons in such a way that they function as associative memory or also called content addressable memory. This is a recurrent network similar to recurrent layer of the hamming network but which can effectively perform the operation of both layer hamming network. The design of recurrent network has always been interesting problems to research and a lot of work is going on present application. In present paper we will discuss about the design of Hopfield Neural Network (HNNs), bidirectional associative memory (BAMs) and multidirectional associative memory (MAMs) for handwritten characters recognition. Recognized characters are Hindi alphabets.

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Studies on an Electronic Analog of a Recurrent Neural Network with Retrieval Phase Weight Adaptations

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2005.p0127 ◽

2005 ◽

Vol 9 (2) ◽

pp. 127-133

Author(s):

Vishwanathan Mohan ◽

◽

Yashwant V. Joshi ◽

Anand Itagi ◽

Garipelli Gangadhar

Keyword(s):

Neural Network ◽

Associative Memory ◽

Character Recognition ◽

Storage Capacity ◽

Hopfield Neural Network ◽

Learning System ◽

Adaptive Law ◽

Stability Issues ◽

Electronic Implementation ◽

Retrieval Phase

It is argued that weight adaptations even during retrieval phase can greatly enhance the performance of a neurodynamic associative memory. Our simulations with an electronic implementation of an associative memory showed that extending the Hopfield dynamics with an appropriate adaptive law in retrieval phase could give rise to significant improvements in storage capacity and computational reliability. Weights, which are supposed to encode the information stored in the Hopfield neural network, are usually held constant once training/storage is complete. In our case, weights also change during retrieval, hence losing information in the process, but resulting in much better retrieval of stored patterns. We describe and characterize the functional elements comprising the network, the learning system, and include the experimental results obtained from applying the network for character recognition in various noisy conditions. Stability issues emerging as a consequence of retrieval phase weight adaptation and implications of weights being used as transitory, intermediary variables are briefly discussed.

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