Text Recognition in Moving Vehicles using Deep learning Neural Networks

Ilayarajaa, K.T.; Vijayakumar, V.; Sugadev, M.; Ravi, T.

doi:10.1109/icais50930.2021.9395980

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Next, we apply the non-negative RESCAL factorization to the tensors X(fake) and X(real). As a result of the factorization, we end up with tables A fake ∈ Ru × r and A real ∈ Ru × r , respectively, through the following non-negative updates that result [ 39 , 43 , 44 ]:

where,

…”

Section: Methodsmentioning

confidence: 99%

English Text Recognition Deep Learning Framework to Automatically Identify Fake News

Xiao-yu

2022

Computational Intelligence and Neuroscience

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Fake news spreading rapidly worldwide is considered one of the most severe problems of modern technology that needs to be addressed immediately. The remarkable increase in the use of social media as a critical source of information combined with the shaking of trust in traditional media, the high speed of digital news dissemination, and the vast amount of information circulating on the Internet have exacerbated the problem of so-called fake news. The present work proves the importance of detecting fake news by taking advantage of the information derived from friendships between users. Specifically, using an innovative deep temporal convolutional network (DTCN) scheme assisted using the tensor factorization non-negative RESCAL method, we take advantage of class-aware rate tables during and not after the factorization process, producing more accurate representations to detect fake news with exceptionally high reliability. In this way, the need to develop automated methods for detecting false information is demonstrated with the primary aim of protecting readers from misinformation.

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where,

…”

Section: Methodsmentioning

confidence: 99%

English Text Recognition Deep Learning Framework to Automatically Identify Fake News

Xiao-yu

2022

Computational Intelligence and Neuroscience

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“…It exhibits commendable accuracy even for vehicles in motion at speeds of up to 10 km/hour. The primary focus of this study revolves around the intricate domain of character recognition, which holds immense potential for further enhancement and exploration in the realm of data analytics [1].…”

Section: Related Workmentioning

confidence: 99%

E-Challan Generation System using ANPR with Desktop Application

Chaudhari¹

2023

IJRASET

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Efficiently overseeing adherence to traffic regulations is an arduous task faced by authorities in light of population growth and increased vehicular activity on roads. The conventional approach to managing road users who break laws involves time-intensive manual processes that interfere with smooth transportation operations. This paper proposes a promising tactic for automating the production of E-challans through incorporation of Automatic Number Plate Recognition (ANPR) technology. By installing mounted cameras with ANPR capabilities alongside CCTV equipment which utilizes image processing together with optical character recognition technology (OCR), swift automated identification of infringing drivers can be achieved through read-outs from their vehicle registration plates. Ultimately, this method seeks to lessen reliance on human resources while enhancing the effectiveness of law enforcement activities

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