Recent advances in large language models have driven major breakthroughs in Text-to-SQL tasks. However, many challenges hinder the use of SQL parsers for cross-language tasks. In this article, we introduce FGCSQL, a novel three-stage pipeline framework to deal with three challenges: cross-language schema linking, SQL parsing potential of LLM, and error propagation in SQL parsers, in which the framework uniquely incorporates a filtering encoder to eliminate irrelevant database schema items, harnessing a pre-trained generative large language model fine-tuned on a carefully structured dataset for enhanced SQL parsing. Finally, a correcting decoder addresses error propagation, culminating in a robust system for semantic parsing tasks. Tested on the CSpider dataset, the FGCSQL showcases a substantial improvement in the exact-set-match (EM) accuracy and execution accuracy (EX) metrics, validating the pipeline’s architecture’s effectiveness in mitigating the challenges typically confronted in Text-to-SQL conversion, especially in cross-lingual contexts. FGCSQL outstrips existing methods in execution precision, indicating the validity of our proposed method.

Table structure recognition, as the most important subtask in image-based table recognition, recognizes the logical relationship between adjacent cells and represents the structured essence of the table. However, in reality, tables often appear in many different styles, such as those containing many blank cells, large row–column spans, missing separator lines, or even no separator lines at all. Different styles present great challenges to table structure recognition. In response to the above situation, we carefully analyzed the structural characteristics of the table and believed that rows and columns are their inherent characteristics. However, row–column annotations are rarely encountered in existing publicly available datasets, and cells with row–column spans are often ignored. Thus, we propose an innovative table structure annotation scheme, where the objects to be annotated include rows, columns, and cells with row–column spans. Furthermore, we released a challenging dataset named Row–Column Segmentation for Table Structure Recognition (RCSTSR), which contains more than 12,000 table images of different styles, each of which is notated with the corresponding mask. Then, on the basis of this dataset, we construct an effective semantic segmentation-based solution for table structure recognition. It consists of two main parts: an improved masked-attention mask transformer model, named TableStructureFormer, and corresponding postprocessing. Among them, the former is responsible for predicting the masks of objects in the table image, and the latter is used to generate the table structure on the basis of the predicted masks. Considering that long-distance feature maps are more useful than local feature maps for row–column segmentation, we propose the dual-path adaptive weighted attention module to aggregate the multilevel long-distance feature maps and adaptively select more input information by introducing enhanced strip pooling and learnable weighted parameters, thereby improving the segmentation performance. In addition, to address the difficulty of perfectly segmenting the details of rows and columns, we propose the deep detail supervision module to guide the segmentation model to learn the detailed feature maps about objects, thereby further correcting their masks. The experimental results show that for row–column segmentation, the mean intersection over union (mIoU) values of TableStructureFormer are 92.15% and 92.46%, respectively, which are significantly greater than those of the existing segmentation models. For table structure recognition, it can also accurately generate table structures according to the segmentation results. The tree edit distance similarity for the table structure (TEDS-Struct), precision, and recall are 91.42%, 90.96%, and 87.73%, respectively, indicating excellent table logic understanding ability. When the position matching thresholds are 0.50 and 0.75, the average accuracy (AP) is 91.50% and 86.32%, respectively, indicating excellent spatial perception ability. In summary, the proposed TableStructureFormer can accurately and robustly implement row–column segmentation and table structure recognition, even when facing different styles of tables.

As one of the most commonly used and important data carriers, tables have the advantages of high structuring, strong readability and strong flexibility. However, in reality, tables usually present various forms, such as Excel, images, etc. Among them, the information in the table image cannot be read directly, let alone further applied. Therefore, the research related to image-based table recognition is crucial. It contains the table structure recognition and the table content recognition. Among them, table structure recognition is the most important and difficult task because the table structure is abstract and changeable. In order to address this problem, we propose an innovative table structure recognition method, named TSRDet (Table Structure Recognition based on object Detection). It includes a row-column detection method, named SACNet (StripAttention-CenterNet) and the corresponding post-processing. SACNet is an improved version of the original CenterNet. The specific improvements include the following: firstly, we introduce the Swin Transformer as the encoder to obtain the global feature map of the image. Then, we propose a plug-and-play row-column attention module, including a channel attention module and a row-column spatial attention module. It improves the detection accuracy of rows and columns by capturing long-range row-column feature maps in the image. After completing the row-column detection, this paper also designs a simple and fast post-processing to generate the table structure based on the row-column detection results. Experimental results show that for row-column detection, SACNet has high detection accuracy, even at a high IoU threshold. Specifically, when the threshold is 0.75, its mAP of row detection and column detection still exceeds 90%, which is 91.40% and 92.73% respectively. In addition, in the comparative experiment with the existing object detection methods, SACNet’s performance was significantly better than that of all others. For table structure recognition, the TEDS-Struct score of TSRDet is 95.7%, which shows competitive performance in table structure recognition, and verifies the rationality and superiority of the proposed method.

Recent advances in large language models have driven major breakthroughs in Text-to-SQL tasks. However, many challenges hinder the use of SQL parsers for cross-language tasks. In this article, we introduce FGCSQL, a novel three-stage pipeline framework to deal with three challenges: cross-language schema linking, SQL parsing potential of LLM, and error propagation in SQL parsers, in which the framework uniquely incorporates a Filtering Encoder to eliminate irrelevant database schema items, harnessing a pre-trained Generative Large Language Model fine-tuned on a carefully structured dataset for enhanced SQL parsing. Finally, a Correcting Decoder addresses error propagation, culminating in a robust system for semantic parsing tasks. Tested on the CSpider dataset, the FGCSQL showcases a substantial improvement in Exact-set-Match(EM) accuracy and EXecution accuracy(EX) metrics, validating the pipeline’s architecture’s effectiveness in mitigating the challenges typically confronted in Text-to-SQL conversion, especially in cross-lingual contexts. FGCSQL outstrips existing methods in execution precision, indicating the validity of our proposed method.