The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex. Despite achieving high performance, they often overfit to the table style in training set, and suffer from significant performance degradation when encountering out-of-distribution tables in other domains. To tackle this problem, we start from the essence of the table, which is a set of text arranged in rows and columns. Based on this, we propose a novel, light-weighted and robust Table Detection method based on Learning Text Arrangement, namely TDeLTA. TDeLTA takes the text blocks as input, and then models the arrangement of them with a sequential encoder and an attention module. To locate the tables precisely, we design a text-classification task, classifying the text blocks into 4 categories according to their semantic roles in the tables. Experiments are conducted on both the text blocks parsed from PDF and extracted by open-source OCR tools, respectively. Compared to several state-of-the-art methods, TDeLTA achieves competitive results with only 3.1M model parameters on the large-scale public datasets. Moreover, when faced with the cross-domain data under the 0-shot setting, TDeLTA outperforms baselines by a large margin of nearly 7%, which shows the strong robustness and transferability of the proposed model.

In this paper, we propose a reliable hybrid positioning methodology by combining the advantages of H∞filter and extreme learning machine (ELM), which addresses GPS outages and uncertain nonlinear drift of MEMS INS simultaneously. A novel parallel-dual-H∞filtering (PDHF) mechanism is proposed to prevent the H∞filter from diverging during GPS outages and to make full use of supplementary observations. The PDHF is composed of an enhanced H∞filter and an auxiliary H∞filter. The enhanced H∞filter is developed by fusing not only GPS information but also supplementary observations, which include yaw angle provided by electronic compass, longitudinal velocity derived from wheel speed sensor, and lateral velocity constrained by assumptions, whereas the auxiliary H∞filter only fuses the supplementary observations. Furthermore, an ELM module with good generalization ability is designed and augmented with the auxiliary H∞filter to constitute a “virtual enhanced H∞filter.” In the case of GPS outages, the “virtual enhanced H∞filter” provides accurate corrections for stand-alone INS. Due to the characteristics of the Hoc filter, the proposed methodology is immune to uncertain nonlinear drift of MEMS INS in nature. To verify the effectiveness of the proposed methodology, road-test experiments with various scenarios were performed. The experimental results indicate that the proposed methodology outperform all the compared counterparts.