The ubiquity of smartphone cameras has led to more and more documents being captured by cameras rather than scanned. Unlike flatbed scanners, photographed documents are often folded and crumpled, resulting in large local variance in text structure. The problem of document rectification is fundamental to the Optical Character Recognition (OCR) process on documents, and its ability to overcome geometric distortions significantly affects recognition accuracy. Despite the great progress in recent OCR systems, most still rely on a pre-process that ensures the text lines are straight and axis aligned. Recent works have tackled the problem of rectifying document images taken in-the-wild using various supervision signals and alignment means. However, they focused on global features that can be extracted from the document's boundaries, ignoring various signals that could be obtained from the document's content. We present CREASE: Content Aware Rectification using Angle Supervision, the first learned method for document rectification that relies on the document's content, the location of the words and specifically their orientation, as hints to assist in the rectification process. We utilize a novel pixel-wise angle regression approach and a curvature estimation side-task for optimizing our rectification model. Our method surpasses previous approaches in terms of OCR accuracy, geometric error and visual similarity.

该研究介绍了一种新的文档矫正方法—— 内容感知矫正算法（Content Aware Rectification using Angle Supervision, CREASE），该算法通过利用文档内容、单词位置及其方向等信号来辅助矫正，采用一种新颖的基于像素角度回归的方法和曲率估计，优化了文档矫正模型。与之前的方法相比，它在OCR准确性、几何误差和视觉相似度方面有明显的优势。

使用角度监督的内容感知矫正