Table detection is a crucial step in document analysis, which involves identifying tables within scanned documents and extracting their structured data. Traditional methods rely on hand-crafted rules and heuristics, but these are limited by their reliance on manual feature engineering and the quality of the training data. In this paper, we propose a novel deep learning-based approach for table detection that leverages reference images to improve accuracy and generalization.
Reference Images
The key innovation of our method is the use of reference images, which are obtained by detecting tables in a set of known-good examples. These reference images provide a rich source of information that can be used to guide the detection process, much like a GPS system provides directions to a destination. By comparing the query image to these reference images, we can determine the presence and location of tables with higher accuracy.
Deep Learning
Our proposed method builds on recent advances in deep learning, particularly convolutional neural networks (CNNs), which have shown great promise in image classification tasks. We use a CNN to learn features from the query image and compare them to the features extracted from the reference images. This allows us to identify tables with high accuracy, even when the query image is corrupted or degraded.
Encoder-Decoder Architecture
Our proposed method utilizes an encoder-decoder architecture, where the encoder compresses the input image into a set of latent features, and the decoder reconstructs the original image from these features. This allows us to learn a compact and informative representation of the input data, which can be used for table detection.
Loss Function
To train our model, we use a loss function that combines the Intersection over Union (IoU) metric with a smooth L1 loss term. The IoU metric measures the overlap between the predicted bounding box and the ground truth bounding box, while the L1 loss term encourages the model to produce sharp predictions. This combination of metrics allows us to balance accuracy and precision in our table detection.
Results
Our proposed method achieves state-of-the-art performance on several benchmark datasets, outperforming traditional table detection methods by a wide margin. We also demonstrate the generalization capabilities of our method by evaluating it on unseen data from multiple sources.
Conclusion
In this paper, we presented a novel deep learning-based approach for table detection that leverages reference images to improve accuracy and generalization. Our proposed method utilizes an encoder-decoder architecture, combined with a loss function that balances IoU and L1 loss terms. We demonstrated the effectiveness of our method on several benchmark datasets and showed its ability to generalize to unseen data from multiple sources. This work has the potential to significantly improve document analysis systems and enable more accurate table detection in a wide range of applications.
