- The paper presents TableBank as a breakthrough dataset, providing 417,234 high-quality labeled tables to overcome limitations of hand-labeled examples.
- It employs a weak supervision method using Word and LaTeX markup to automatically extract table boundaries and structure for enhanced model training.
- Experiments using Faster R-CNN and sequence generation models show high F1 and BLEU scores, validating improved performance and cross-domain generalization.
TableBank: A Benchmark Dataset for Table Detection and Recognition
The paper "TableBank: A Benchmark Dataset for Table Detection and Recognition" presents a comprehensive large-scale dataset aimed at enhancing the capabilities of deep learning models in the domain of table analysis within digital documents. This paper introduces TableBank, which comprises 417,234 high-quality labeled tables sourced through a novel weak supervision approach from Word and LaTeX documents available online.
Dataset Composition and Methodology
Existing table detection methodologies generally rely on limited datasets composed of hand-labeled examples, which restricts the generalization capabilities of these models. TableBank seeks to address this limitation by providing a significantly larger dataset. The authors exploit the intrinsic markup characteristics of Word and LaTeX documents to generate labeled datasets automatically. These documents are processed to obtain bounding boxes for tables using XML tag manipulation in Word documents and LaTeX source code adjustments.
The resulting dataset, which is publicly available, is divided into subsets for table detection and table structure recognition tasks, offering diverse examples across languages and formats, thus promising improved robustness in modeling.
Baseline Models and Evaluation Metrics
The research utilizes established deep learning architectures as baseline models to validate the efficacy of TableBank. For table detection, the Faster R-CNN architecture, enhanced with ResNeXt variants, serves as the core model. The evaluation on TableBank yields high F1 scores, notably on datasets drawn from the same document type as the training data.
In terms of table structure recognition, a sequence generation approach is deployed using an image-to-markup model. The BLEU score, commonly employed to assess sequence generation tasks, serves as the evaluation metric. The findings indicate reliable performance, albeit with decreased accuracy for more complex table structures.
Results and Cross-Domain Performance
The paper reveals that models trained with TableBank perform adequately within the same domain, but exhibit reduced performance across domains. This suggests intrinsic differences in visual characteristics between Word and LaTeX document tables. However, models trained on combined datasets show better cross-domain generalization, underscoring the utility of extensive and varied training data.
Additionally, experiments on the ICDAR 2013 dataset demonstrate that models derived from TableBank outperform many existing systems, thus validating its relevance as a comprehensive training resource.
Implications and Future Directions
The creation of TableBank is positioned to significantly influence the application of deep learning techniques in document analysis. Through large-scale training data, it empowers the development of models that are not only capable of accurate table detection and recognition but are also more adaptable to diverse document formats encountered in real-world applications.
Moving forward, additional expansions could include the incorporation of multi-class labels, encompassing other document elements such as figures and headings, which could pave the way for holistic document understanding systems. Furthermore, exploring domain adaptation techniques could mitigate cross-domain performance discrepancies, enhancing the versatility of models trained on TableBank.
In conclusion, TableBank represents a valuable contribution to the field of document analysis, promising to catalyze advancements in neural network applications for structured data extraction from electronic documents.