TRR360D: A dataset for 360 degree rotated rectangular box table detection
This provides a new benchmark for researchers studying rotated table detection algorithms, though it is incremental as it adapts existing datasets and formats.
The paper tackles the scarcity and high annotation costs of rotated image table detection datasets by creating the TRR360D dataset, which includes 600 training images with 977 instances and 240 test images with 499 instances, based on ICDAR2019MTD and DOTA formats.
To address the problem of scarcity and high annotation costs of rotated image table detection datasets, this paper proposes a method for building a rotated image table detection dataset. Based on the ICDAR2019MTD modern table detection dataset, we refer to the annotation format of the DOTA dataset to create the TRR360D rotated table detection dataset. The training set contains 600 rotated images and 977 annotated instances, and the test set contains 240 rotated images and 499 annotated instances. The AP50(T<90) evaluation metric is defined, and this dataset is available for future researchers to study rotated table detection algorithms and promote the development of table detection technology. The TRR360D rotated table detection dataset was created by constraining the starting point and annotation direction, and is publicly available at https://github.com/vansin/TRR360D.