论文标题
阿拉伯语脚本的有效的无关语言的多指OCR
An Efficient Language-Independent Multi-Font OCR for Arabic Script
论文作者
论文摘要
光学特征识别（OCR）是从扫描文档的图像中提取数字化文本的过程。虽然OCR系统已经以多种语言成熟，但它们仍然存在着包裹的字母（例如阿拉伯语）的草书语言的缺点。本文提出了一个完整的阿拉伯OCR系统，该系统将阿拉伯语Naskh脚本的扫描图像作为输入，并生成相应的数字文档。我们的阿拉伯OCR系统由以下模块组成：预处理，文字级特征提取，角色分割，角色识别和后处理。本文还提出了一种优于最新分割算法的改进的非依赖性字符分割算法。最后，本文提出了一个针对角色识别任务的神经网络模型。该系统已经在几个开放的阿拉伯语料库数据集上进行了实验，其平均角色分割精度为98.06％，角色识别精度为99.89％，整体系统精度为97.94％，与最先进的阿拉伯OCR系统相比，取得了出色的成果。
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.
