Fast localization and rectification of documents folded into thirds

The ubiquitous usage of smartphones makes camera-captured document images as widely used as scanned ones as the input of a modern document recognition system. A document captured by a smartphone camera may appear mechanically distorted in the image creating the need for an image rectification step. The present paper considers a particular case of document image distortions. Specifically, if a business document is sent via postal service, it may need to be folded to fit the envelope. Once the document is taken out of the envelope and unfolded, its geometric shape is distorted in a very particular pattern. Since the most popular envelope formats in Europe and America require the document to be folded into thirds, this case is considered in this paper. We propose a novel content-independent model-based algorithm for the localization and geometrical rectification of documents folded into thirds. Our algorithm outperforms current SOTA rectification methods on the recently published dataset FDI by key rectification accuracy metrics (AD and CER) and is able to rectify documents held in hand. Moreover, it can be executed on a mobile CPU and has a reasonable execution time: it takes only about 17 ms to localize a document and about 110 ms to projectively rectify it. So it makes it possible to embed the proposed algorithm into document recognition systems designed for on-device acquisition.