Document Layout Analysis on BaDLAD Dataset: A Comprehensive MViTv2 Based Approach
This work addresses automated information extraction from documents, but it is incremental as it applies an existing method to a specific dataset with minor enhancements.
The paper tackled document layout analysis by training an MViTv2 transformer with cascaded mask R-CNN on the BaDLAD dataset to extract text boxes, paragraphs, images, and tables, achieving a training loss of 0.2125 and a mask loss of 0.19 after training on 20,365 document images for 36 epochs.
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.