提出端到端阿拉伯文手写印刷体识别方案，支持结构化输出
Invizo: Arabic Handwritten Document Optical Character Recognition Solution
- 结合CNN与Transformer处理笔迹风格、粗细等变化
- 手写文本达7.91%字符错误率，印刷体仅0.59%错误率
- 适用于真实场景的阿拉伯文书稿识别，通用性强
将阿拉伯文字图像转换为纯文本是学术界和工业界广泛研究的课题。由于阿拉伯文字的复杂性，手写与印刷体识别面临巨大挑战。本文提出一种端到端解决方案，可识别阿拉伯手写体、印刷体及阿拉伯数字，并以结构化形式呈现结果。在文本检测任务中，模型达到81.66%精确率、78.82%召回率和79.07%F-measure。所提识别模型融合先进CNN特征提取与Transformer序列建模，有效应对笔迹风格、笔画粗细、对齐差异及噪声干扰。评估表明，该模型在印刷体上实现0.59%字符错误率（CER）和1.72%词错误率（WER），手写体则为7.91% CER和31.41% WER。整体方案已验证在真实OCR任务中的可靠性，集成检测、识别及其他特征提取匹配算法，具备通用性，适用于任意阿拉伯文手写或印刷文档，具有实际应用价值。
原文摘要 · Abstract (English)
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.
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