Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.

Automated summary

This paper proposes a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals using convolutional neural networks (CNNs). The simulation results demonstrate high accuracy and performance, with a recognition rate close to 100%.