Synthetic Data Augmentation for Table Detection: Re-evaluating TableNet's Performance with Automatically Generated Document Images
Document pages captured by smartphones or scanners often contain tables, yet manual extraction is slow and error-prone. We introduce an automated LaTeX-based pipeline that synthesizes realistic two-column pages with visually diverse table layouts and aligned ground-truth masks. The generated corpus augments the real-world Marmot benchmark and enables a systematic resolution study of TableNet. Training TableNet on our synthetic data achieves a pixel-wise XOR error of 4.04% on our synthetic test set with a 256x256 input resolution, and 4.33% with 1024x1024. The best performance on the Marmot benchmark is 9.18% (at 256x256), while cutting manual annotation effort through automation.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationTable DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Do Generative Metrics Predict YOLO Performance? An Evaluation Across Models, Augmentation Ratios, and Dataset Complexity
Synthetic images are increasingly used to augment object-detection training sets, but reliably evaluating a synthetic dataset before training remains difficult: standard global generative metrics (e.g., FID) often do not…
Evaluating Fake Music Detection Performance Under Audio Augmentations
With the rapid advancement of generative audio models, distinguishing between human-composed and generated music is becoming increasingly challenging. As a response, models for detecting fake music have been proposed. In…
DeepFake DetectionEvaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery
Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine learning classifiers. The impact of this…
DiagnosticDiversityMS-SSIMSSIMEvaluating a Synthetic Image Dataset Generated with Stable Diffusion
We generate synthetic images with the "Stable Diffusion" image generation model using the Wordnet taxonomy and the definitions of concepts it contains. This synthetic image database can be used as training data for data …
Data Augmentationimage-classificationImage ClassificationImage GenerationEnhancing Maritime Object Detection in Real-Time with RT-DETR and Data Augmentation
Maritime object detection faces essential challenges due to the small target size and limitations of labeled real RGB data. This paper will present a real-time object detection system based on RT-DETR, enhanced by employ…
Real-Time Object DetectionData Augmentation