Java character recognition using Hopfield network

Widodo, Wahyu; Priambodo, Rinto; Adhi, Bambang Prasetya

doi:10.1088/1757-899x/434/1/012034

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…The Hopfield neural network is a two-state information processing model first described in 1982 [17]. The dynamical system exhibits numerous physical properties relevant to the study of spin glasses (disordered magnets) [18], biological neural networks [19], and computer science (including circuit design [20], the traveling salesman problem [21], image segmentation [22], and character recognition [23]).…”

Section: The Hopfield Networkmentioning

confidence: 99%

Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network

Vavilala¹

2020

Eur. J. Phys.

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The synthesis of high-performance computing (particularly graphics processing units), cloud computing services (like Google Colab), and high-level deep learning frameworks (such as PyTorch) has powered the burgeoning field of artificial intelligence. While these technologies are popular in the computer science discipline, the physics community is less aware of how such innovations, freely available online, can improve research and education. In this tutorial, we take the Hopfield network as an example to show how the confluence of these fields can dramatically accelerate physicsbased computer simulations and remove technical barriers in implementing such programs, thereby making physics experimentation and education faster and more accessible. To do so, we introduce the cloud, the GPU, and AI frameworks that can be easily repurposed for physics simulation.We then introduce the Hopfield network and explain how to produce large-scale simulations and visualizations for free in the cloud with very little code (fully self-contained in the text). Finally, we suggest programming exercises throughout the paper, geared towards advanced undergraduate students studying physics, biophysics, or computer science.

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Section: The Hopfield Networkmentioning

confidence: 99%

Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network

Vavilala¹

2020

Eur. J. Phys.

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“…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]. Recognition of handwritten Chinese character using Hopfield neural networks of stroke extraction and pre-processing feature set of stroke and row-column assignments character matching [14].…”

Section: Odia Numeralsmentioning

confidence: 99%

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

Jena¹,

Pradhan²,

Biswal³

et al. 2019

IJRTE

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Odia character and digits recognition area are vital issues of these days in computer vision. In this paper a Hopfield neural network designe 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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Java character recognition using Hopfield network

Cited by 2 publications

References 3 publications

Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network

Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network

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

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