{"total":25,"rows":[{"id":"2203.01017","title":"TableFormer: Table Structure Understanding with Transformers","created":"2022-03-02","cited_by_count":94,"area":null},{"id":"2305.03393","title":"Optimized Table Tokenization for Table Structure Recognition","created":"2023-05-05","cited_by_count":34,"area":null},{"id":"2208.04921","title":"TSRFormer: Table Structure Recognition with Transformers","created":"2022-08-09","cited_by_count":27,"area":null},{"id":"2303.14884","title":"A large-scale dataset for end-to-end table recognition in the wild","created":"2023-03-27","cited_by_count":24,"area":null},{"id":"2303.03730","title":"LORE: Logical Location Regression Network for Table Structure Recognition","created":"2023-03-07","cited_by_count":19,"area":null},{"id":"2208.11168","title":"Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks","created":"2022-08-23","cited_by_count":17,"area":null},{"id":"2309.14962","title":"GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction","created":"2023-09-26","cited_by_count":13,"area":null},{"id":"2303.04384","title":"SEMv2: Table Separation Line Detection Based on Instance Segmentation","created":"2023-03-08","cited_by_count":12,"area":null},{"id":"2303.11615","title":"Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer","created":"2023-03-21","cited_by_count":12,"area":null},{"id":"2401.01522","title":"LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-training","created":"2024-01-03","cited_by_count":8,"area":null},{"id":"2208.11203","title":"Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents","created":"2022-08-23","cited_by_count":6,"area":null},{"id":"2503.11932","title":"SPRINT: Script-agnostic Structure Recognition in Tables","created":"2025-03-15","cited_by_count":6,"area":null},{"id":"2304.10439","title":"A Study on Reproducibility and Replicability of Table Structure Recognition Methods","created":"2023-04-20","cited_by_count":4,"area":null},{"id":"2207.00589","title":"SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection","created":"2022-07-03","cited_by_count":3,"area":null},{"id":"2505.05446","title":"Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding","created":"2025-05-08","cited_by_count":3,"area":null},{"id":"2201.01654","title":"TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets","created":"2022-01-05","cited_by_count":2,"area":null},{"id":"2304.11810","title":"PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis","created":"2023-04-24","cited_by_count":2,"area":null},{"id":"2312.00699","title":"Rethinking Detection Based Table Structure Recognition for Visually Rich Document Images","created":"2023-12-01","cited_by_count":1,"area":null},{"id":"2409.05125","title":"PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction","created":"2024-09-08","cited_by_count":1,"area":null},{"id":"2504.17522","title":"TableCenterNet: A one-stage network for table structure recognition","created":"2025-04-24","cited_by_count":1,"area":null},{"id":"2604.02880","title":"InstructTable: Improving Table Structure Recognition Through Instructions","created":"2026-04-03","cited_by_count":1,"area":null},{"id":"2604.16070","title":"TableSeq: Unified Generation of Structure, Content, and Layout","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2605.22422","title":"FastTab: A Fast Table Recognizer with a Tiny Recursive Module and 1D Transformers","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2606.18721","title":"Rethinking the Pointer Loss in Table Structure Recognition: Geometry-Aware Pointer Loss for Spatial Locality","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2609.17458","title":"Tables Decoded: DELTA for Structure, TARQA for Understanding","created":"2026-09-15","cited_by_count":0,"area":null}],"next_cursor":null}