Document Layout Analysis on BaDLAD Dataset: A Comprehensive MViTv2 Based
  Approach
Ashrafur Rahman Khan Asif Azad
In the rapidly evolving digital era, the analysis of document layouts plays a
pivotal role in automated information extraction and interpretation. In our
work, we have trained MViTv2 transformer model architecture with cascaded mask
R-CNN on BaDLAD dataset to extract text box, paragraphs, images and tables from
a document. After training on 20365 document images for 36 epochs in a 3 phase
cycle, we achieved a training loss of 0.2125 and a mask loss of 0.19. Our work
extends beyond training, delving into the exploration of potential enhancement
avenues. We investigate the impact of rotation and flip augmentation, the
effectiveness of slicing input images pre-inference, the implications of
varying the resolution of the transformer backbone, and the potential of
employing a dual-pass inference to uncover missed text-boxes. Through these
explorations, we observe a spectrum of outcomes, where some modifications
result in tangible performance improvements, while others offer unique insights
for future endeavors.