Abstract
Converting images of Arabic text into plain text is a widely researched topic in academia and industry. However, recognition of Arabic handwritten and printed text presents difficult challenges due to the complex nature of variations of the Arabic script. This work proposes an end-to-end solution for recognizing Arabic handwritten, printed, and Arabic numbers and presents the data in a structured manner. We reached 81.66% precision, 78.82% Recall, and 79.07% F-measure on a Text Detection task that powers the proposed solution. The proposed recognition model incorporates state-of-the-art CNN-based feature extraction, and Transformer-based sequence modeling to accommodate variations in handwriting styles, stroke thicknesses, alignments, and noise conditions. The evaluation of the model suggests its strong performances on both printed and handwritten texts, yielding 0.59% CER and & 1.72% WER on printed text, and 7.91% CER and 31.41% WER on handwritten text. The overall proposed solution has proven to be relied on in real-life OCR tasks. Equipped with both detection and recognition models as well as other Feature Extraction and Matching helping algorithms. With the general purpose implementation, making the solution valid for any given document or receipt that is Arabic handwritten or printed. Thus, it is practical and useful for any given context.
Abstract (translated)
将阿拉伯文图像转换为纯文本是学术界和工业界广泛研究的主题。然而，由于阿拉伯文字体的复杂性及其变体，识别手写和印刷的阿拉伯文文本面临着巨大的挑战。本文提出了一种端到端解决方案，用于识别阿拉伯手写、印刷及阿拉伯数字，并以结构化的方式呈现数据。在支撑该方案的文字检测任务上，我们达到了81.66%的精度（Precision）、78.82%的召回率（Recall）和79.07%的F值（F-measure）。提出的识别模型结合了最先进的基于CNN的特征提取技术和基于Transformer的序列建模技术，以适应书写风格、笔画粗细、对齐方式以及噪音条件的变化。模型评估显示其在印刷文本上表现良好，CER（字符错误率）为0.59%，WER（词错误率）为1.72%；在手写文本上，CER达到7.91%，WER则为31.41%。整体解决方案已被证明在实际OCR任务中可靠有效，并配备了检测和识别模型以及其他特征提取与匹配算法。通过通用实施方式，使其适用于任何给定的阿拉伯文手写或印刷文档。因此，在任何情境下都具有实用性和价值。
URL
https://arxiv.org/abs/2502.05277
