[论文解读] Doctors Handwritten Prescription Recognition System In Multi Language Using Deep Learning

G Pavithiran, Sharan Padmanabhan+4 more|arXiv (Cornell University)|Oct 21, 2022

Handwritten Text Recognition Techniques被引用 6

一句话总结

本文提出了一种使用深度学习的多语言手写处方识别系统，以解决医生字迹潦草及地区性语言差异的问题。该系统采用图像预处理、单词分割，以及支持Unicode的CNN-RNN-LSTM架构进行多语言文本识别，通过模糊搜索和市场篮子分析实现准确的处方解读，从而优化药物输出。

ABSTRACT

Doctors typically write in incomprehensible handwriting, making it difficult for both the general public and some pharmacists to understand the medications they have prescribed. It is not ideal for them to write the prescription quietly and methodically because they will be dealing with dozens of patients every day and will be swamped with work.As a result, their handwriting is illegible. This may result in reports or prescriptions consisting of short forms and cursive writing that a typical person or pharmacist won't be able to read properly, which will cause prescribed medications to be misspelled. However, some individuals are accustomed to writing prescriptions in regional languages because we all live in an area with a diversity of regional languages. It makes analyzing the content much more challenging. So, in this project, we'll use a recognition system to build a tool that can translate the handwriting of physicians in any language. This system will be made into an application which is fully autonomous in functioning. As the user uploads the prescription image the program will pre-process the image by performing image pre-processing, and word segmentations initially before processing the image for training. And it will be done for every language we require the model to detect. And as of the deduction model will be made using deep learning techniques including CNN, RNN, and LSTM, which are utilized to train the model. To match words from various languages that will be written in the system, Unicode will be used. Furthermore, fuzzy search and market basket analysis are employed to offer an end result that will be optimized from the pharmaceutical database and displayed to the user as a structured output.

研究动机与目标

• 开发一种自主系统，能够准确识别多种地区性语言的手写处方。
• 解决因医生字迹潦草导致药房用药错误的问题。
• 实现对临床环境中常见的草书或缩写形式处方的识别。
• 通过Unicode实现多语言支持，以适应医疗场景中多样的地区性语言。
• 利用模糊搜索和市场篮子分析优化处方解读，实现与药物数据库的对齐。

提出的方法

• 使用降噪和二值化技术对输入处方图像进行预处理。
• 执行单词分割，以分离出用于分析的独立处方组件。
• 训练一种结合卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络的混合深度学习模型。
• 使用Unicode编码以支持不同脚本和区域语言的多语言文本识别。
• 应用模糊搜索算法，将识别出的文本与数据库中的已知药物术语进行匹配。
• 采用市场篮子分析，基于常见处方模式建议或验证药物关联性。

实验结果

研究问题

• RQ1 如何使深度学习模型在多种地区性语言中有效识别手写处方？
• RQ2 哪些预处理和分割技术能提升在低质量或草书处方图像中的识别准确率？
• RQ3 结合CNN、RNN和LSTM架构在多语言医疗文本识别中能带来多大程度的性能提升？
• RQ4 基于Unicode的编码如何支持单一统一系统中多语言处方的识别？
• RQ5 模糊搜索和市场篮子分析能否提升识别处方的可靠性和临床相关性？

主要发现

• 该系统成功通过统一的深度学习流程处理并解读了多种地区性语言的手写处方。
• 图像预处理和单词分割显著提升了后续文本识别步骤的清晰度和准确率。
• CNN-RNN-LSTM架构在识别多种脚本中的草书和缩写医学文本方面表现出稳健性能。
• Unicode集成实现了多语言文本的一致表示，支持印度语及其他地区性语言的识别。
• 模糊搜索和市场篮子分析增强了系统建议准确药物替代品或确认的能力。
• 该应用展示了自主功能，能从原始处方图像生成结构化、机器可读的输出。