Seal Removal Based on Generative Adversarial Gated Convolutional Network
Seals on invoices and documents seriously affect the accuracy of text recognition,so seal elimination techniques play an important role in the pre-processing of document analysis,and document enhancement.However,threshold-based methods and deep learning-based methods suffer from incomplete seal elimination and modification of background pixels.Thus,this paper pro-poses a two-stage seal elimination network,SealErase.The first stage is a U-shaped segmentation network for generating bina-rized masks with seal position,and the second stage is an inpainting network for refined seal elimination.Due to the lack of availa-ble public paired datasets for seal elimination,existing methods cannot design pixel-level evaluation metrics to measure the quality of the generated images.Moreover,training the neural network using paired training sets can effectively improve the performance of the network.To this end,this paper constructs a high-simulated seal elimination dataset containing 8 000 samples,taking into account the generalisation to real scenes and the robustness to noise.The seals are divided into two types:seals in real document images and synthetic seals.In order to objectively evaluate the performance of SealErase,it devises a comprehensive evaluation metric based on the image generation quality and the recognition accuracy of characters obscured by seals to evaluate the elimina-tion performance of the SealErase network.The existing seal elimination methods are compared on the seal elimination dataset,and the experimental results show that the SealErase network improve the peak signal to noise ratio by 26.79％and the mean structural similarity by 4.48％in the evaluation metric of image generation quality compared to the state-of-the-art methods.Af-ter seal elimination by SealErase network,the accuracy of recognition of characters obscured by seals is improved by 38.86％.Ex-perimental results show that SealErase is equally effective in eliminating seals and preserving the obscured characters in real scenes.
Seal removalImage inpaintingSeal synthesisGenerative adversarial networksGated ConvolutionsSealErase
NETL
NSTL
万方数据
