scholarly journals Handwritten Pashto Characters Dataset for Optical Character Recognition

2021 ◽  
Author(s):  
wahidullah mudaser ◽  
Jonathan H. Chan
Keyword(s):  
Computational Intelligence ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Joint Symposium ◽  
Optical Character ◽  
Research Students ◽  
Young Researchers ◽  
Support Research

<div>This work was presented at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.</div><div><br></div><div><div>The Pashto character database developed in this work is available at <a href="https://github.com/mudaser37/pashtoCharacterDataset" rel="noreferrer noopener" target="_blank">GitHub - mudaser37/pashtoCharacterDataset</a></div></div>

scholarly journals Handwritten Pashto Characters Dataset for Optical Character Recognition

2021 ◽  
Author(s):  
wahidullah mudaser ◽  
Jonathan H. Chan
Keyword(s):  
Computational Intelligence ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Joint Symposium ◽  
Optical Character ◽  
Research Students ◽  
Young Researchers ◽  
Support Research

<div>This work was presented at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.</div><div><br></div><div><div>The Pashto character database developed in this work is available at <a href="https://github.com/mudaser37/pashtoCharacterDataset" rel="noreferrer noopener" target="_blank">GitHub - mudaser37/pashtoCharacterDataset</a></div></div>

scholarly journals Learning from Others: Daily COVID-19 Cases Prediction in India using Ensembles of LSTM-RNNs

2021 ◽  
Author(s):  
Debasrita Chakraborty ◽  
Debayan Goswami ◽  
Susmita Ghosh ◽  
Jonathan H. Chan ◽  
Ashish Ghosh
Keyword(s):  
Computational Intelligence ◽  
Joint Symposium ◽  
Research Students ◽  
Learning From Others ◽  
Young Researchers ◽  
Support Research

This work was presented at the 10th Joint Symposium on Computational Intelligence (JSCI10), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.

scholarly journals Learning from Others: Daily COVID-19 Cases Prediction in India using Ensembles of LSTM-RNNs

2021 ◽  
Author(s):  
Debasrita Chakraborty ◽  
Debayan Goswami ◽  
Susmita Ghosh ◽  
Jonathan H. Chan ◽  
Ashish Ghosh
Keyword(s):  
Computational Intelligence ◽  
Joint Symposium ◽  
Research Students ◽  
Learning From Others ◽  
Young Researchers ◽  
Support Research

This work was presented at the 10th Joint Symposium on Computational Intelligence (JSCI10), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.

scholarly journals Hand Foot and Mouth Rash Detection Using Deep Convolution Neural Network

2021 ◽  
Author(s):  
Naqibullah Vakili ◽  
Jonathan H. Chan ◽  
Worarat Krathu ◽  
Nipat Phattarakijtham ◽  
Kazuya Hirata
Keyword(s):  
Neural Network ◽  
Computational Intelligence ◽  
Convolution Neural Network ◽  
Joint Symposium ◽  
Foot And Mouth ◽  
Deep Convolution Neural Network ◽  
Research Students ◽  
Young Researchers ◽  
Support Research

<div>This work was presented at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.</div>

scholarly journals Hand Foot and Mouth Rash Detection Using Deep Convolution Neural Network

2021 ◽  
Author(s):  
Naqibullah Vakili ◽  
Jonathan H. Chan ◽  
Worarat Krathu ◽  
Nipat Phattarakijtham ◽  
Kazuya Hirata
Keyword(s):  
Neural Network ◽  
Computational Intelligence ◽  
Convolution Neural Network ◽  
Joint Symposium ◽  
Foot And Mouth ◽  
Deep Convolution Neural Network ◽  
Research Students ◽  
Young Researchers ◽  
Support Research

<div>This work was presented at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.</div>

Computational Intelligence Foundations and Principles

2017 ◽  
pp. 62-85
Author(s):  
Murugan Sethuraman Sethuraman
Keyword(s):  
Computer Science ◽  
Computational Intelligence ◽  
Intelligent Agents ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Intelligent Agent ◽  
Rational Agents ◽  
Abstract Thought ◽  
Optical Character ◽  
The Brain

AI has been defined in different ways, including the abilities for abstract thought, understanding, communication, reasoning, learning, retaining, planning, and solving. Intelligence is most widely studied in humans, but has also been observed in animals and plants. AI is the intelligence of machines or the simulation of intelligence in machines. AI is both the intelligence of machines and the branch of Computer Science which aims to create it, through the study and design of intelligent agents or rational agents, where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. Achievements include constrained and well-defined problems such as games, crossword-solving and optical character recognition. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception, and the ability to move and manipulate objects. In the field of AI there is no consensus on how closely the brain should be simulated.

A Structured Method for the Recognition of Complex Historical Tables

1997 ◽  
Vol 9 (1-3) ◽  
pp. 58-77
Author(s):  
Vitaly Kliatskine ◽  
Eugene Shchepin ◽  
Gunnar Thorvaldsen ◽  
Konstantin Zingerman ◽  
Valery Lazarev
Keyword(s):  
Nineteenth Century ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Source Material ◽  
Historical Sources ◽  
Tax Assessment ◽  
Optical Character ◽  
Algorithmic Model ◽  
Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

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