Real-Time Arabic Text-Reading for Visually Impaired People

Visually impaired (VI) people suffer from many difficulties when accessing printed material using existing technologies. These problems may include text alignment, focus, accuracy, software processing speed, mobility, and efficiency. Current technologies such as flatbed scanners and OCR programs need to scan an entire page. Recently, VI people prefer mobile devices because of their handiness and accessibility, but they have problems with focusing the mobile camera on the printed material. In this paper, a real-time Arabic text-reading prototype for VI people is proposed. It is based on using a wearable device for a hand finger. It is designed as a wearable ring attached to a tiny webcam device. The attached camera captures the printed Arabic text and passes it to the Arabic OCR system. Finally, the recognized characters are translated into speech using the text-to-speech (TTS) technology. Experimental results demonstrate the feasibility of the proposed prototype. It achieved an accuracy of 95.86% for Arabic character recognition and 98.5% for English character recognition.

Corpus-based technique for improving Arabic OCR system

<p><span>An optical character recognition (OCR) refers to a process of converting the text document images into editable and searchable text. OCR process poses several challenges in particular in the Arabic language due to it has caused a high percentage of errors. In this paper, a method, to improve the outputs of the Arabic Optical character recognition (AOCR) Systems is suggested based on a statistical language model built from the available huge corpora. This method includes detecting and correcting non-word and real words error according to the context of the word in the sentence. The results show that the percentage of improvement in the results is up to (98%) as a new accuracy for AOCR output. </span></p>

An Efficient Language-Independent Multi-Font OCR for Arabic Script

Optical Character Recognition (OCR) is the process of extracting digitized text from images of scanned documents. While OCR systems have already matured in many languages, they still have shortcomings in cursive languages with overlapping letters such as the Arabic language. This paper proposes a complete Arabic OCR system that takes a scanned image of Arabic Naskh script as an input and generates a corresponding digital document. Our Arabic OCR system consists of the following modules: Pre-processing, Word-level Feature Extraction, Character Segmentation, Character Recognition, and Post-processing. This paper also proposes an improved font-independent character segmentation algorithm that outperforms the state-of-the-art segmentation algorithms. Lastly, the paper proposes a neural network model for the character recognition task. The system has experimented on several open Arabic corpora datasets with an average character segmentation accuracy 98.06%, character recognition accuracy 99.89%, and overall system accuracy 97.94% achieving outstanding results compared to the state-of-the-art Arabic OCR systems.