Word spotting in handwritten Arabic documents using bag-of-descriptors

2016 ◽  
Vol 9 ◽  
pp. 1349-1357 ◽  
Author(s):  
Youssef Elfakir ◽  
Ghizlane Khaissidi ◽  
Mostafa Mrabti ◽  
Driss Chenouni ◽  
Mounim El Yacoubi
Keyword(s):  
Word Spotting ◽  
Handwritten Arabic

scholarly journals Segmentation-free Word Spotting for Handwritten Arabic Documents

2016 ◽  
Vol 4 (1) ◽  
pp. 6 ◽  
Author(s):  
Ghizlane Khaissidi ◽  
Youssef Elfakir ◽  
Mostafa Mrabti ◽  
Mounîm El Yacoubi ◽  
Driss Chenouni ◽  
...  
Keyword(s):  
Word Spotting ◽  
Free Word ◽  
Handwritten Arabic

Word spotting for handwritten Arabic documents using Harris detector

2016 ◽  
Author(s):  
Youssef Elfakiri ◽  
Ghizlane Khaissidi ◽  
Mostafa Mrabti ◽  
Driss Chenouni ◽  
Mounim El Yacoubi
Keyword(s):  
Word Spotting ◽  
Harris Detector ◽  
Handwritten Arabic

scholarly journals The impact of the image processing in the indexation system

2019 ◽  
Vol 9 (5) ◽  
pp. 4311
Author(s):  
Youssef Elfakir ◽  
Ghizlane Khaissidi ◽  
Mostafa Mrabti ◽  
Driss Chenouni
Keyword(s):  
Sliding Window ◽  
Similarity Score ◽  
Local Region ◽  
Word Spotting ◽  
Processing Step ◽  
Codebook Size ◽  
Window Approach ◽  
The Impact ◽  
Processing Steps ◽  
Handwritten Arabic

This paper presents an efficient word spotting system applied to handwritten Arabic documents, where images are represented with bag-of-visual-SIFT descriptors and a sliding window approach is used to locate the regions that are most similar to the query by following the query-by-example paragon. First, a pre-processing step is used to produce a better representation of the most informative features. Secondly, a region-based framework is deployed to represent each local region by a bag-of-visual-SIFT descriptors. Afterward, some experiments are in order to demonstrate the codebook size influence on the efficiency of the system, by analyzing the curse of dimensionality curve. In the end, to measure the similarity score, a floating distance based on the descriptor’s number for each query is adopted. The experimental results prove the efficiency of the proposed processing steps in the word spotting system.

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.

3D word spotting using leap motion sensor

2021 ◽  
Author(s):  
Partha Pratim Roy ◽  
Pradeep Kumar ◽  
Shweta Patidar ◽  
Rajkumar Saini
Keyword(s):  
Motion Sensor ◽  
Leap Motion ◽  
Word Spotting
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