Classification of Hijaiyah Letters Using Hybrid CNN-CatBoost

Abstract

In this digital era, character recognition technology and letter classification are topics that are increasingly attracting attention, especially in the context of developing applications for learning Arabic, text processing, and artificial intelligence systems. There have been many previous studies examining this topic. However, there are still many opportunities to develop models for classifying hijaiyah letters to help many people learn to write hijaiyah letters.
 In this study, building a hijaiyah letter classification model using Hybrid-CNN and CatBoost, where CNN is used as a feature extractor and CatBoost as a classifier. CNN will be trained first to become a feature extractor and the results of the CNN model will be used as a feature extractor to create feature representation for training and testing CatBoost model. The AHDC dataset was used in this study and succeeded in achieving an accuracy value of 96.07%. Although it has not been able to compete with previous research, the Hybrid-CNN model with CatBoost has good potential in the future.