Spatial Dependency Parsing for 2D Document Understanding
Table 2: The parsing accuracy table on CORD, CORD-R, CORD-Two-Column, and Namecard-22k datasets.
['Model', 'task', 'dev-oracle  [ITALIC] F1', 'dev-oracle acc', 'dev  [ITALIC] F1', 'dev acc', 'test-oracle  [ITALIC] F1', 'test-oracle acc', 'test  [ITALIC] F1', 'test acc']
We compare our model to POT that extracts information through BIO-tagging after the serialization of whole text segments though feature-engineering. Since, the performance of POT is not measured on CORD and not open-sourced, we prepared our own implementation of POT and performs experiments on CORD by ourselves. The oracle score measures the OCR-independent score over human annotated text segments whereas other score is computed by parsing text segments obtained from OCR task. The result shows that SPADE♠ has competent performance compared to POT (1st, 2nd, and 3rd rows) despite of the fact that our-model is serializer-free.