A Pen Based Intelligent System for Educating Arabic Handwriting
Creators: name: Loey, Mohamed
Publisher: name: figshare
Year: 2020
Subjects: subject: Applied Computer Science
subject: Artificial Intelligence and Image Processing
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: 80199 Artificial Intelligence and Image Processing not elsewhere classified
subject: 80102 Artificial Life
subject: 80401 Coding and Information Theory
subject: Computer Software
subject: 80104 Computer Vision
subject: 80304 Concurrent Programming
subject: 80105 Expert Systems
subject: Information Systems
subject: 80502 Mobile Technologies
subject: 80305 Multimedia Programming
subject: 80107 Natural Language Processing
subject: 80306 Open Software
subject: 80307 Operating Systems
subject: 80109 Pattern Recognition and Data Mining
subject: 80308 Programming Languages
subject: 80708 Records and Information Management (excl. Business Records and Information Management)
subject: FOS: Media and communications
subject: FOS: Media and communications

description: The main objective of this thesis is to build an intelligent tutor system to<br>recognize and diagnose children handwritten mistakes. Handwritten recognition system has been an important area due to its applications in several<br>fields such as optical character recognition, license Plate recognition, etc.<br>This work is focusing on the recognition as a part of handwritten Arabic<br>characters and digits recognition that faces several challenges, including the<br>unlimited variation in human handwriting and the large public databases. The<br>proposed system was designed and developed for automatically recognizing<br>handwritten Arabic characters and digits using deep learning architectures.<br>The system also detects and diagnoses Arabic preschool children handwritten<br>characters based on immediate feedbacks. We show how to automate this task,<br>creating a system that checks the handwriting errors such as stroke sequence<br>errors, stroke direction error, stroke position error, and extra stroke error.<br>For Arabic handwritten digits recognition , this work presents a deep<br>learning architecture called Convolutional Neural Network (CNN). LeNet-5<br>model was implemented by two convolutional, two pooling layers ,and two<br>classification layers. CNN based on LeNet-5 was used to train and test the<br>MADBase database (Arabic handwritten digits images) that contains 10k<br>testing images and 60k training images. The CNN based LeNet-5 outcomes<br>an average accuracy 88%. We improve the performance of the classification<br>using unsupervised learning approach called Stacked AutoEncoder (SAE)<br>for Arabic handwritten digits categorization. SAE shows that the use of<br>SAE leads to significant improvements across different machine-learning<br>viii<br>classification algorithms. Stacked autoencoder is giving an average accuracy of 98.5%. We improve the performance of handwritten digits using a<br>convolutional neural network (CNN). The optimization methods were implemented to increase the performance of CNN. A comparison is held amongst<br>the results, and it is shown by the end that the use of convolutional neural<br>network led to significant improvements across different machine learning<br>classification algorithms. Convolutional neural nework was trained and tested<br>on MADBase database. The proposed CNN approach is describe as follows<br>: INPUT → CONV → RELU → POOL → CONV → RELU → POOL →<br>FC → RELU → FC → Output. Moreover, the improved convolutional neural<br>network is giving an average recognition accuracy of 99.15%.<br>For Arabic handwritten characters recognition , this work presents an<br>unsupervised learning approach using SAE designed for Arabic handwritten<br>characters categorization. SAE was trained and tested using our proposed<br>dataset that contains 3360 testing images and 13440 training images. We<br>show that the use of SAE leads to bad classification outcomes. SAE is giving<br>an average accuracy of 64%. This thesis presents a supervised learning<br>approach using convolutional neural network. The convolutional neural<br>nework was trained and tested using our database that contains 16800 dataset.<br>The proposed CNN approach is described as follows : INPUT → CONV →<br>RELU → POOL → CONV → RELU → POOL → FC → RELU → FC →<br>Output. Moreover, the convolutional neural network is giving an average<br>recognition accuracy of 94.85%.<br>Finally, an intelligent tutor application was built for Arabic preschool<br>children called Intelligent Arab Teaching. Intelligent Arab Teaching system<br>allows Arab children to practice at anytime and anywhere. The Intelligent<br>Arab Teaching provides a useful feedback to Arab children to correct their<br>mistakes. Experimental results indicate that the proposed intelligent system<br>successfully detect handwritten strokes errors with immediate feedback. <br>