Document Change Detection With Hierarchical Patch Comparison

Contract documents can be modified just before signing, after the consensus, with the intention of defrauding the other party, which can have serious consequences for the deal. To prevent the issue, we propose a method to detect document changes between a scanned final document and its original electronic file using image-based comparison. Our method first finds the most appropriate augmentation for various document changes, such as rotations, contrast, ratio, or brightness changes which can occur while scanning documents. Then, we employ a hierarchical search strategy from large patches to small patches in a sliding window manner, which can reduce the computational complexity to compare all the details of the documents using the deep learning model. We built a new dataset of original-scanned document pair for the validation of our method. In the experiments, we show that our method outperforms the previous approaches using segmentation and character recognition models, even when the document suffers from both non-lingual and lingual changes.