用深度学习精准提取手写处方中的药名，提升医疗自动化水平。
Leveraging Deep Learning with Multi-Head Attention for Accurate Extraction of Medicine from Handwritten Prescriptions
- 先用Mask R-CNN定位药名区域，再用带多头注意力的TrOCR识别文字。
- 在标准测试集上字符错误率低至1.4%，识别准确率高。
- 专为巴基斯坦多地手写处方设计，适合医疗数字化场景使用。
由于医生手写处方的笔迹风格和格式差异大，从中提取药品名称极具挑战。本文提出一种结合Mask R-CNN与基于Transformer的光学字符识别（TrOCR）的方法，引入多头注意力和位置嵌入增强识别能力。构建了一个涵盖巴基斯坦多个地区多样手写处方的新数据集，用于模型微调以适应不同书写风格。首先由Mask R-CNN对处方图像进行分割，聚焦药物区域；随后TrOCR模型对截取文本进行转录。转录结果与预存药品数据库匹配以实现精准识别。该方法在标准基准测试中达到1.4%的字符错误率（CER），展现出作为高效可靠自动化药品提取工具的潜力。
原文摘要 · Abstract (English)
Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription formats. This paper presents a robust method for extracting medicine names using a combination of Mask R-CNN and Transformer-based Optical Character Recognition (TrOCR) with Multi-Head Attention and Positional Embeddings. A novel dataset, featuring diverse handwritten prescriptions from various regions of Pakistan, was utilized to fine-tune the model on different handwriting styles. The Mask R-CNN model segments the prescription images to focus on the medicinal sections, while the TrOCR model, enhanced by Multi-Head Attention and Positional Embeddings, transcribes the isolated text. The transcribed text is then matched against a pre-existing database for accurate identification. The proposed approach achieved a character error rate (CER) of 1.4% on standard benchmarks, highlighting its potential as a reliable and efficient tool for automating medicine name extraction.
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