Arabic Handwritten Word Recognition Based on Stationary Wavelet Transform Technique using Machine Learning

This paper is aimed at improving the performance of the word recognition system (WRS) of handwritten Arabic text by extracting features in the frequency domain using the Stationary Wavelet Transform (SWT) method using machine learning, which is a wavelet transform approach created to compensate for the absence of translation invariance in the Discrete Wavelets Transform (DWT) method. The proposed SWT-WRS of Arabic handwritten text consists of three main processes: word normalization, feature extraction based on SWT, and recognition. The proposed SWT-WRS based on the SWT method is evaluated on the IFN/ENIT database applying the Gaussian, linear, and polynomial support vector machine, the k-nearest neighbors, and ANN classifiers. ANN performance was assessed by applying the Bayesian Regularization (BR) and Levenberg-Marquardt (LM) training methods. Numerous wavelet transform (WT) families are applied, and the results prove that level 19 of the Daubechies family is the best WT family for the proposed SWT-WRS. The results also confirm the effectiveness of the proposed SWT-WRS in improving the performance of handwritten Arabic word recognition using machine learning. Therefore, the suggested SWT-WRS overcomes the lack of translation invariance in the DWT method by eliminating the up-and-down samplers from the proposed machine learning method.

A Survey on Arabic Handwritten Script Recognition Systems

The optical character recognition (OCR) system is still an active research field in pattern recognition. Such systems can identify, recognize and distinguish electronically between characters and texts, printed or handwritten. They can also do a transformation of such data type into machine-processable form to facilitate the interaction between user and machine in various applications. In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. This paper also presents a survey of the works that have been carried out over recent years. Finally, some open issues and potential research directions have been highlighted

A Survey on Arabic Handwritten Script Recognition Systems

The optical character recognition (OCR) system is still an active research field in pattern recognition. Such systems can identify, recognize and distinguish electronically between characters and texts, printed or handwritten. They can also do a transformation of such data type into machine-processable form to facilitate the interaction between user and machine in various applications. In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. This paper also presents a survey of the works that have been carried out over recent years. Finally, some open issues and potential research directions have been highlighted

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%.