AI-based document preprocessing for optical character recognition

基于人工智能的光学字符识别文档预处理

• 批准号: 567474-2021
• 负责人: Ray, Nilanjan
• 金额: $ 2.35万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720824
• 关键词: AI; based; document; preprocessing; optical

Even though automated optical character recognition (OCR) is now a mature technology, it is facing a new challenge these days: sources of documents are now coming from pictures taken by multiple sources including mobile cameras. These images contain significant variations in lighting, distortion, and other artifacts that lowers the accuracy of OCR algorithms. In this research, model OCR as an unknown-box and improve image quality by deep learning-based algorithms suitable for the unknown-box. The unknown-box assumption accurately reflects a reality where end-users do not have access to the components of the OCR, e.g., proprietary software. Our proposed method to improve image quality relies on state-of-the-art advancements in image analysis and computer vision algorithms. The industry partner for the research is Inuit, Inc. and Intuit, Canada who are one of the leaders in automated document processing.

尽管自动光学字符识别（OCR）现在已经是一项成熟的技术，但它现在面临着新的挑战：文档来源现在来自包括移动相机在内的多个来源拍摄的图片。这些图像包含明显的光照变化、失真和其他伪像，从而降低了 OCR 算法的准确性。在本研究中，将 OCR 建模为未知框，并通过适合未知框的基于深度学习的算法来提高图像质量。未知框假设准确地反映了最终用户无法访问 OCR 组件（例如专有软件）的现实。我们提出的提高图像质量的方法依赖于图像分析和计算机视觉算法的最先进进步。该研究的行业合作伙伴是 Inuit, Inc. 和加拿大 Intuit，它们是自动化文档处理领域的领导者之一。