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Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things (IoT) systems, much research has been done in using machine learning methods to recognize IoT sensors data. This is particularly the case for optical character recognition of handwritten scripts. Recognizing text in images has several useful applications, including content-based image retrieval, searching and document archiving. The Arabic language is one of the mostly used tongues in the world. However, Arabic text recognition in imagery is still very much in the nascent stage, especially handwritten text. This is mainly due to the language complexities, different writing styles, variations in the shape of characters, diacritics, and connected nature of Arabic text. In this paper, two deep learning models were proposed. The first model was based on a sequence-to-sequence recognition, while the second model was based on a fully convolution network. To measure the performance of these models, a new dataset, called QTID (Quran Text Image Dataset) was devised. This is the first Arabic dataset that includes Arabic diacritics. It consists of 309,720 different 192 × 64 annotated Arabic word images, which comprise 2,494,428 characters in total taken from the Holy Quran. The annotated images in the dataset were randomly divided into 90%, 5%, and 5% sets for training, validation, and testing purposes, respectively. Both models were set up to recognize the Arabic Othmani font in the QTID. Experimental results show that the proposed methods achieve state-of-the-art outcomes. Furthermore, the proposed models surpass expectations in terms of character recognition rate, F1-score, average precision, and recall values. They are superior to the best Arabic text recognition engines like Tesseract and ABBYY FineReader.
Badry et al. (Fri,) studied this question.