Abstract
This paper proposes an effective approach to automatic recognition of printed Arabic numerals which are extracted from digital images. First, the input image is normalized and pre-processed to an acceptable form. From the preprocessed image, components of the words are segmented into individual objects representing different numbers. Second, the numerical recognition is performed using an expert system based on a set of if-else rules, where each set of rules represents the categorization of each number. Finally, rigorous experiments are carried out on 226 random Arabic numerals selected from 40 images of Iraqi car plate numbers. The proposed method attained an accuracy of 97%.
Highlights
Automatic character recognition is becoming very important in many practical applications such as postcode identification and car plate number recognition
An effective algorithm for Arabic offline print written number recognition is proposed in this research
Several preprocessing operations are initially applied to the input image such as conversion to binary image, noise removal, morphological filtering, and segmentation before entering the data to the recognition system
Summary
Automatic character recognition is becoming very important in many practical applications such as postcode identification and car plate number recognition. An automated process that involves the application of a camera to capture the plate numbers and recognize them using a predictive model, would be beneficial in terms of computational time, and ease the amount of human effort required for such task. An effective method for Arabic character recognition is presented, which is applicable to Kurdish and Persian languages [1]. In this paper, a technique to recognize Arabic Numerals by using handcrafted features and expert system for decision making is proposed This method involves extracting geometric features for each object to be further classified using expert system, which is discussed in-depth in the subsequent sections.
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