针对传统药品生产过程中人工检测药品包装三期信息（批号、生产日期和有效期）效率低、错误率高的问题，设计了一种基于光学字符识别（OCR）的自动化药品信息识别系统，以提高药品质量控制水平，满足监管合规性要求。系统采用创新的硬件配置与改进的软件技术相结合的方式，硬件部分设计了适配不同规格药品包装的检测平台，结合光度立体技术捕获高质量压印字符图像；软件部分对文本检测和识别算法进行了改进，采用改进的DBNet算法引入可变形卷积模块和特征金字塔增强模块提升对复杂场景文字的检测能力，并在文本识别模块中集成了可变形注意力机制，增强对复杂字符的区分能力。实验验证表明，系统在处理1200张低质量药品信息图像时，识别准确率达到89.0%，相比传统ABINet模型提高了3.7个百分点。同时，在实际生产线测试中，系统对5种药品包装类型的检测准确率达到98%以上，平均检测耗时约为人工检测的1/4。本系统显著提高了药品三期信息的检测和识别效率，解决了传统方法易错难控的问题，能够满足实际药品生产线的应用需求，为药品信息的质量监管提供了可靠的技术支持。

To address the inefficiency and high error rates of manual inspection of three-phase information (batch number, production date, and expiry date) on pharmaceutical packaging in traditional production processes, this study designs an automated pharmaceutical information recognition system based on Optical Character Recognition (OCR). The system aims to enhance quality control and ensure regulatory compliance. It integrates innovative hardware configurations with improved software technologies. On the hardware side, a detection platform was designed to accommodate pharmaceutical packages of various specifications, leveraging photometric stereo technology to capture high-quality embossed character images. On the software side, the text detection and recognition algorithms were improved. The enhanced DBNet algorithm incorporates deformable convolution modules and feature pyramid enhancement modules to improve detection capabilities for complex text scenarios, while the text recognition module integrates deformable attention mechanisms to enhance the distinction of complex characters. Experimental results show that the system achieves an accuracy of 89.0% when processing 1200 low-quality pharmaceutical information images, an improvement of 3.7 percentage points compared to the traditional ABINet model. Additionally, in real production line testing, the system achieved a detection accuracy of 98%-100% for five types of pharmaceutical packaging, with an average detection time approximately one-quarter that of manual inspection. This system significantly improves the efficiency and accuracy of pharmaceutical three-phase information detection and recognition, overcoming the limitations of traditional methods and providing reliable technical support for quality regulation in pharmaceutical production.