论文标题
检测蒙版可改善OCR的嘈杂文档
Detection Masking for Improved OCR on Noisy Documents
论文作者
论文摘要
光学特征识别（OCR），从扫描文档中提取文本信息的任务是一项重要且广泛使用的技术，用于数字化和索引物理文档。现有技术在干净的文档中表现良好，但是当文档在视觉上降低或存在非文本元素时，可能会严重影响OCR质量，特别是由于检测错误。在本文中，我们提出了一个改进的检测网络，该检测网络具有掩盖系统，以提高在文档上执行的OCR质量。通过从图像过滤非文本元素，我们可以利用文档级OCR来合并上下文信息以改善OCR结果。我们对公开可用的数据集执行统一的评估，以证明我们方法的有用性和广泛的适用性。此外，我们通过专门调整以改善检测结果的独特硬性组件，介绍并公开我们的合成数据集，并评估从其使用情况下获得的好处
Optical Character Recognition (OCR), the task of extracting textual information from scanned documents is a vital and broadly used technology for digitizing and indexing physical documents. Existing technologies perform well for clean documents, but when the document is visually degraded, or when there are non-textual elements, OCR quality can be greatly impacted, specifically due to erroneous detections. In this paper we present an improved detection network with a masking system to improve the quality of OCR performed on documents. By filtering non-textual elements from the image we can utilize document-level OCR to incorporate contextual information to improve OCR results. We perform a unified evaluation on a publicly available dataset demonstrating the usefulness and broad applicability of our method. Additionally, we present and make publicly available our synthetic dataset with a unique hard-negative component specifically tuned to improve detection results, and evaluate the benefits that can be gained from its usage
