Analysis on Preprocessing Techniques for Offline Handwritten Recognition

2019 ◽  
pp. 546-553
Author(s):  
Krupashankari S. Sandyal ◽  
Y. C. Kiran
Keyword(s):  
Handwritten Recognition

scholarly journals New Attributes Extraction System for Arabic Autograph as Genuine and Forged through a Classification Techniques

2021 ◽  
Author(s):  
Anwar Yahya Ebrahim ◽  
Hoshang Kolivand
Keyword(s):  
Principal Component Analysis ◽  
Decision Tree ◽  
Discrete Cosine Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction System ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
The World ◽  
Handwritten Recognition

The authentication of writers, handwritten autograph is widely realized throughout the world, the thorough check of the autograph is important before going to the outcome about the signer. The Arabic autograph has unique characteristics; it includes lines, and overlapping. It will be more difficult to realize higher achievement accuracy. This project attention the above difficulty by achieved selected best characteristics of Arabic autograph authentication, characterized by the number of attributes representing for each autograph. Where the objective is to differentiate if an obtain autograph is genuine, or a forgery. The planned method is based on Discrete Cosine Transform (DCT) to extract feature, then Spars Principal Component Analysis (SPCA) to selection significant attributes for Arabic autograph handwritten recognition to aid the authentication step. Finally, decision tree classifier was achieved for signature authentication. The suggested method DCT with SPCA achieves good outcomes for Arabic autograph dataset when we have verified on various techniques.

Using Diversity in Classifier Set Selection for Arabic Handwritten Recognition

2010 ◽  
pp. 235-244 ◽  
Author(s):  
Nabiha Azizi ◽  
Nadir Farah ◽  
Mokhtar Sellami ◽  
Abdel Ennaji
Keyword(s):  
Selection For ◽  
Handwritten Recognition

scholarly journals Deep Learning Algorithms for Arabic Handwriting Recognition: A Review

2018 ◽  
Vol 7 (3.20) ◽  
pp. 344 ◽  
Author(s):  
Ahmed AL-Saffar ◽  
Suryanti Awang ◽  
Wafaa AL-Saiagh ◽  
Sabrina Tiun ◽  
A S. Al-khaleefa
Keyword(s):  
Deep Learning ◽  
Handwriting Recognition ◽  
Arabic Language ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Learning Approaches ◽  
Visual Science ◽  
Science Major ◽  
Handwritten Recognition ◽  
Recognition Technique

 Computer vision (CV) refers to the study of the computer simulation of human visual science. Major task of CV is to collect images (or video) so that they could be used for analysis, gathering information, and making decisions or judgements. CV has greatly progressed and developed in the past few decades. In recent years, deep learning (DL) approaches have won several contests in pattern recognition and machine learning. (DL) dramatically improved the state-of-the-art in visual object recognition, object detection, handwritten recognition and many other domains. Handwritten recognition technique is one of this tasks that targeted to extract the text from documents or another images type. In contrast to the English domain, there are a limited works on the Arabic language that achieved satisfactory results, Due to the Arabic language cursive nature that induces many technical difficulties. This paper highlighted the pre-processing and binarization methods that have been used in the literature along with proposed numerous directions for developing. We review the various current deep learning approaches and tools used for Arabic handwritten recognition (AHWR), identified challenges along this line of this research, and gives several recommendations including a framework based (DL) that is particularly applicable for dealing with cursive nature languages.  

Local patterns for offline Arabic handwritten recognition

2020 ◽  
Vol 16 (2) ◽  
pp. 203
Author(s):  
Yasser Qawasmeh ◽  
Sari Awwad ◽  
Ahmed Fawzi Otoom ◽  
Feras Hanandeh ◽  
Emad Abdallah
Keyword(s):  
Local Patterns ◽  
Handwritten Recognition
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