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

Abstract

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-ofthe-art Arabic OCR systems.

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

“…Publicly available scanned image datasets are tested by [40], e.g., the WATAN and APTI datasets with extensive vocabularies. The datasets are split into a training set and a testing set, where training data contain 282,000 word images and 1,200,000 characters images while testing 5500 words, and 100,500 characters are used.…”

An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition

“…In some cases, the secondary ligature may not exist. Different Urdu characters join with each other and form different ligatures based on joiner rules [29]. These ligatures often take the form of diacritics like dots or dashes that are appended somewhere with the character.…”

“…These secondary ligatures are stored separately in a list and are marked during the entire training process. When the training procedure invokes the distance scaling module, we allot specific weights to different ligatures based on the joiner rules [29]. These weights allow the model to separate similar characters with only a discriminating diacritic, which helps in improving the per-class classification accuracy and the subsequent overall accuracy.…”