Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition
This work addresses the challenge of Arabic handwritten digit recognition, which has applications in various fields, but it is incremental as it applies an existing method to a specific dataset.
The paper tackles the problem of recognizing handwritten Arabic digits by proposing a stacked autoencoder approach, achieving an average accuracy of 98.5% on the MADBase database.
This paper presents a new unsupervised learning approach with stacked autoencoder (SAE) for Arabic handwritten digits categorization. Recently, Arabic handwritten digits recognition has been an important area due to its applications in several fields. This work is focusing on the recognition part of handwritten Arabic digits recognition that face several challenges, including the unlimited variation in human handwriting and the large public databases. Arabic digits contains ten numbers that were descended from the Indian digits system. Stacked autoencoder (SAE) tested and trained the MADBase database (Arabic handwritten digits images) that contain 10000 testing images and 60000 training images. We show that the use of SAE leads to significant improvements across different machine-learning classification algorithms. SAE is giving an average accuracy of 98.5%.