Recognition of Handwritten Arabic (Indian) Numerals Using Freeman's Chain Codes and Abductive Network Classifiers

2010 ◽  
Author(s):  
Isah A. Lawal ◽  
Radwan E. Abdel-Aal ◽  
Sabri A. Mahmoud
Keyword(s):  
Chain Codes ◽  
Abductive Network ◽  
Handwritten Arabic

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

scholarly journals A Secret-Key Image Steganography Technique using Random Chain Codes

2019 ◽  
Vol 10 (4) ◽  
pp. 731 ◽  
Author(s):  
Mohammed Abbas Fadhil Al-Husainy ◽  
Diaa Mohammed Uliyan
Keyword(s):  
Image Steganography ◽  
Secret Key ◽  
Chain Codes

The Global Diffusion of Supply Chain Codes of Conduct: Market, Nonmarket, and Time-Dependent Effects

2019 ◽  
pp. 000765031987365 ◽  
Author(s):  
Thomas G. Altura ◽  
Anne T. Lawrence ◽  
Ronald M. Roman
Keyword(s):  
Supply Chain ◽  
Home Country ◽  
Codes Of Conduct ◽  
Original Data ◽  
Nongovernmental Organization ◽  
Market Mechanisms ◽  
Data Set ◽  
Chain Codes ◽  
Labor Institutions ◽  
Corporate Social

Why and how have supply chain codes of conduct diffused among lead firms around the globe? Prior research has drawn on both institutional and stakeholder theories to explain the adoption of codes, but no study has modeled adoption as a temporally dynamic process of diffusion. We propose that the drivers of adoption shift over time, from exclusively nonmarket to eventually market-based mechanisms as well. In an analysis of an original data set of more than 1,800 firms between the years 2006 and 2015, we find that strong nonmarket labor institutions in a firm’s home country are critical to initiating and sustaining the diffusion process. Market mechanisms, such as investor scrutiny and brand risk, emerge as important later. Contrary to prior research, we did not find a significant effect from nongovernmental organization (NGO) pressure. We conclude that markets for corporate social responsibility can and do arise, but only after they are effectuated by nonmarket institutions.

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