ClusterTabNet: Supervised clustering method for table detection and table structure recognition
We present a novel deep-learning-based method to cluster words in documents which we apply to detect and recognize tables given the OCR output. We interpret table structure bottom-up as a graph of relations between pairs of words (belonging to the same row, column, header, as well as to the same table) and use a transformer encoder model to predict its adjacency matrix. We demonstrate the performance of our method on the PubTables-1M dataset as well as PubTabNet and FinTabNet datasets. Compared to the current state-of-the-art detection methods such as DETR and Faster R-CNN, our method achieves similar or better accuracy, while requiring a significantly smaller model.
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Datasets
PubTables-1M
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Methods
Absolute Position Encodings •
Adam •
BPE •
Convolution •
Dense Connections •
Detr •
Dropout •
Faster R-CNN •
Feedforward Network •
Label Smoothing •
Layer Normalization •
Linear Layer •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
RoIPool •
RPN •
Scaled Dot-Product Attention •
Softmax •
Transformer
