[论文解读] Classification of Documents Extracted from Images with Optical Character Recognition Methods

Ömer Aydın|arXiv (Cornell University)|Jun 15, 2021

Handwritten Text Recognition Techniques被引用 7

一句话总结

本文提出了一种混合文档分类系统，结合光学字符识别（OCR）与朴素贝叶斯算法，对扫描或拍摄的文档进行分类。使用C#在Visual Studio的Windows平台上实现，该方法通过两种OCR技术处理图像，提取文本，并以高精度将其分类到预定义类别中，展示了在实际应用中自动化文档处理的可行性。

ABSTRACT

Over the past decade, machine learning methods have given us driverless cars, voice recognition, effective web search, and a much better understanding of the human genome. Machine learning is so common today that it is used dozens of times a day, possibly unknowingly. Trying to teach a machine some processes or some situations can make them predict some results that are difficult to predict by the human brain. These methods also help us do some operations that are often impossible or difficult to do with human activities in a short time. For these reasons, machine learning is so important today. In this study, two different machine learning methods were combined. In order to solve a real-world problem, the manuscript documents were first transferred to the computer and then classified. We used three basic methods to realize the whole process. Handwriting or printed documents have been digitalized by a scanner or digital camera. These documents have been processed with two different Optical Character Recognition (OCR) operation. After that generated texts are classified by using Naive Bayes algorithm. All project was programmed in Microsoft Visual Studio 12 platform on Windows operating system. C# programming language was used for all parts of the study. Also, some prepared codes and DLLs were used.

研究动机与目标

• 开发一种自动化系统，用于对从图像中提取的手写体和印刷体文档进行分类。
• 解决使用OCR与机器学习处理现实世界中文档图像中质量与版式差异带来的挑战。
• 评估结合多种OCR方法与概率分类器以提升分类准确率的有效性。
• 使用可访问的工具与编程环境，实现一个实用的端到端解决方案，用于文档数字化与分类。

提出的方法

• 使用两种不同的光学字符识别（OCR）方法处理扫描或数字捕获的文档图像，以提取文本内容。
• 对从图像中提取的文本进行预处理与归一化，以提高分类输入质量。
• 应用朴素贝叶斯算法，基于概率特征加权，将提取的文本分类到预定义的文档类别中。
• 整个处理流程使用C#在Windows平台的Microsoft Visual Studio环境中实现。
• 集成第三方类库（DLL）与代码模块，以支持OCR功能与文本处理。
• 系统在手写文档数据集上进行训练与评估，性能通过标准分类指标进行测量。

实验结果

研究问题

• RQ1 双OCR方法在从文档图像中提升文本提取准确率方面的效果如何？
• RQ2 朴素贝叶斯分类器能否基于OCR提取的文本可靠地分类文档类型？
• RQ3 结合多种OCR技术对整体分类性能有何影响？
• RQ4 使用C#与Visual Studio等标准开发工具实现此类系统的可行性如何？
• RQ5 图像质量在多大程度上影响分类流程的准确率？

主要发现

• 双OCR方法提高了文本提取的可靠性，尤其在低质量或噪声较多的文档图像中表现更优。
• 朴素贝叶斯分类器在测试数据集上实现了高分类准确率，证明其在文档类型识别中的有效性。
• 在C#环境中集成OCR与机器学习，实现了功能完整且可部署的文档分类系统。
• 使用现成的DLL与类库显著减少了开发时间与复杂度。
• 该系统在处理印刷体与手写体文档时表现出较强的鲁棒性，且预处理需求极少。
• 本研究证实，将OCR与概率分类相结合，是现实场景中自动化文档处理的可行解决方案。