Handwriting recognition

Physics

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Machine learning

Philosophy

Artificial intelligence

Component (thermodynamics)

Natural language processing

Geometry

Handwriting

Character (mathematics)

Computer science

Linguistics

Intelligent character recognition

Feature extraction

Computational Science and Engineering

Speech recognition

Character recognition

Artificial neural network

Convolutional neural network

Arabic

Thermodynamics

Neural Computing and Applications

Automatic handwriting recognition is an important component for many applications in various fields. It is a challenging problem that has received a lot of attention in the past three decades. Research has focused on the recognition of Latin languages’ handwriting. Fewer studies have been done for the Arabic language. In this paper, we present a new dataset of Arabic letters written exclusively by children aged 7–12 which we call Hijja. Our dataset contains 47,434 characters written by 591 participants. In addition, we propose an automatic handwriting recognition model based on convolutional neural networks (CNN). We train our model on Hijja, as well as the Arabic Handwritten Character Dataset (AHCD) dataset. Results show that our model’s performance is promising, achieving accuracies of 97% and 88% on the AHCD dataset and the Hijja dataset, respectively, outperforming other models in the literature.

Arabic handwriting recognition system using convolutional neural network