Huang Siyuan, Yongping Xiong, Wu Guibin. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Existing document binarization methods can not extract stroke edges finely, mainly due to the fair-treatment nature of vanilla convolutions and the extraction of stroke edges without adequate supervision by boundary-related information. In this paper, we formulate text extraction as the learning of gating values and propose a novel end-to-end gated convolutions-based network (GDB) to solve the problem of imprecise stroke edge extraction. The gated convolutions are applied to selectively extract the features of strokes with different attention. Firstly, a coarse sub-network with an extra edge branch is trained to get more precise feature maps by feeding a priori mask and edge. Secondly, a refinement sub-network is cascaded to refine the output of the first stage by gated convolutions based on the sharp edge. For global information, GDB also contains a multi-scale operation to combine local and global features. Experimental results show that our proposed methods outperform the SOTA methods in terms of all metrics on average over all DIBCO datasets from 2009 to 2019 and achieve top ranking on six benchmark datasets. Available codes: https://github.com/Royalvice/GDB.

Removing degradation from document images not only improves their visual quality and readability, but also enhances the performance of numerous automated document analysis and recognition tasks. However, existing regression-based methods optimized for pixel-level distortion reduction tend to suffer from significant loss of high-frequency information, leading to distorted and blurred text edges. To compensate for this major deficiency, we propose DocDiff, the first diffusion-based framework specifically designed for diverse challenging document enhancement problems, including document deblurring, denoising, and removal of watermarks and seals. DocDiff consists of two modules: the Coarse Predictor (CP), which is responsible for recovering the primary low-frequency content, and the High-Frequency Residual Refinement (HRR) module, which adopts the diffusion models to predict the residual (high-frequency information, including text edges), between the ground-truth and the CP-predicted image. DocDiff is a compact and computationally efficient model that benefits from a well-designed network architecture, an optimized training loss objective, and a deterministic sampling process with short time steps. Extensive experiments demonstrate that DocDiff achieves state-of-the-art (SOTA) performance on multiple benchmark datasets, and can significantly enhance the readability and recognizability of degraded document images. Furthermore, our proposed HRR module in pre-trained DocDiff is plug-and-play and ready-to-use, with only 4.17M parameters. It greatly sharpens the text edges generated by SOTA deblurring methods without additional joint training. Available codes: https://github.com/Royalvice/DocDiff https://github.com/Royalvice/DocDiff.

Abstract Using deep learning networks to recognize the table attracts lots of attention. However, due to the lack of high-quality table datasets, the performance of using deep learning networks is limited. Therefore, TableRobot has been proposed, an automatic annotation method for heterogeneous tables. To be more specific, the annotations of table consist of the coordinates of the item block and the mapping relationship between item blocks and table cells. In order to transform the task, we successfully design an algorithm based on the greedy approach to find the optimum solution. To evaluate the performance of TableRobot, we check the annotation data of 3000 tables collected from the LaTex documents in arXiv.com , and the result shows that TableRobot can generate table annotation datasets with the accuracy of 93.2%. Besides, the table annotation data is feed into GraphTSR which is a state-of-the-art table recognition graph neural network, and the F1 value of the network has increased by nearly 10% compared with before.

Business Process Execution Language for Web Services is a language, which can be used to define abstract and executable processes. It has became to be the defacto standard of Web Service composition. However, the security aspect of access control is explicitly mentioned to be outside the scope of BPEL. This paper focuses on the implementation of access controls in the BPEL-based process. The existing Task-Based Access Control model was extended. The definition of authorization unit was modified and new types of authorization units were added. Moreover the mapping from the process defined by BPEL to TBAC model was implemented and an approach of using TBAC in BPEL was put forward. The future work was pointed out in the end of the paper.