一种通过使用自然语言处理进行后处理来改进光学字符识别的新型流水线
A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing
AI总结：
本文提出一种端到端流水线，先对手写或印刷文本执行OCR，再利用自然语言处理进行后处理，以解决相似字符和手写变体导致的识别准确率有限问题。
摘要：
光学字符识别（OCR）技术在书籍和非结构化文档的数字化中得到了应用，同时也在移动统计、执法、交通、安全系统等其他领域有所应用。最先进的方法在处理车牌、商店名称等印刷文本的OCR方面表现良好。然而，对于印刷教科书和手写文本等应用，现有技术的准确性有限。其原因可能在于相似外观的字符以及手写字符的变体。由于这些问题仅靠OCR技术难以解决，我们提出了一种使用自然语言处理（NLP）工具的后处理方法。本工作提出了一种端到端流水线，首先对手写或印刷文本执行OCR，然后使用NLP提高其准确性。
英文摘要：
Optical Character Recognition (OCR) technology finds applications in digitizing books and unstructured documents, along with applications in other domains such as mobility statistics, law enforcement, traffic, security systems, etc. The state-of-the-art methods work well with the OCR with printed text on license plates, shop names, etc. However, applications such as printed textbooks and handwritten texts have limited accuracy with existing techniques. The reason may be attributed to similar-looking characters and variations in handwritten characters. Since these issues are challenging to address with OCR technologies exclusively, we propose a post-processing approach using Natural Language Processing (NLP) tools. This work presents an end-to-end pipeline that first performs OCR on the handwritten or printed text and then improves its accuracy using NLP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
