RAPTOR: Refined Approach for Product Table Object Recognition

2025-02-19

AI Summary

DETR-based models (e.g., TATR) suffer from industrial-grade errors—such as region mislocalization and column overlap—in product table detection (TD) and table structure recognition (TSR), primarily due to diverse, unstructured document layouts. To address this, we propose RAPTOR, a modular post-processing framework tailored for product tables (e.g., invoices, quotations). Its key contributions are: (1) the first configurable, domain-specific post-processing architecture for product tables; (2) integration of geometric correction, row/column alignment, and cell merging modules to refine coarse model outputs; and (3) adoption of a genetic algorithm for end-to-end automatic hyperparameter optimization on proprietary datasets. Evaluated on multiple private product table benchmarks, RAPTOR achieves significant F1-score improvements over baseline methods, while maintaining robustness on public benchmarks—including DOCILE and ICDAR 2013/2019. Ablation studies confirm the effectiveness and complementarity of each module.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Extracting tables from documents is a critical task across various industries, especially on business documents like invoices and reports. Existing systems based on DEtection TRansformer (DETR) such as TAble TRansformer (TATR), offer solutions for Table Detection (TD) and Table Structure Recognition (TSR) but face challenges with diverse table formats and common errors like incorrect area detection and overlapping columns. This research introduces RAPTOR, a modular post-processing system designed to enhance state-of-the-art models for improved table extraction, particularly for product tables. RAPTOR addresses recurrent TD and TSR issues, improving both precision and structural predictions. For TD, we use DETR (trained on ICDAR 2019) and TATR (trained on PubTables-1M and FinTabNet), while TSR only relies on TATR. A Genetic Algorithm is incorporated to optimize RAPTOR's module parameters, using a private dataset of product tables to align with industrial needs. We evaluate our method on two private datasets of product tables, the public DOCILE dataset (which contains tables similar to our target product tables), and the ICDAR 2013 and ICDAR 2019 datasets. The results demonstrate that while our approach excels at product tables, it also maintains reasonable performance across diverse table formats. An ablation study further validates the contribution of each module in our system.

Problem

• Enhances table extraction accuracy
• Optimizes table structure recognition
• Addresses diverse table format challenges

Innovation

• Modular post-processing system
• Genetic Algorithm optimization
• DETR and TATR integration