SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation

TL;DR

SwinDocSegmenter introduces a unified transformer-based model for end-to-end document instance segmentation, achieving state-of-the-art results on multiple benchmarks with high accuracy and efficiency.

Contribution

It presents a novel transformer encoder-decoder architecture with contrastive training and mixed query selection for improved document segmentation.

Findings

Achieves higher segmentation accuracy than existing methods.

Performs well across diverse document datasets.

Operates efficiently with less than one billion parameters.

Abstract

Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layouts in document images. The method adapts a contrastive training with a mixed query selection for anchor initialization in the decoder. Later on, it performs a dot product between the obtained query embeddings and the pixel embedding map (coming from the encoder) for semantic reasoning. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ), and TableBank demonstrate that our model with SwinL backbone achieves better segmentation performance than the existing state-of-the-art approaches with the average precision of \textbf{93.72}, \textbf{54.39}, \textbf{84.65} and \textbf{98.04} respectively under one billion parameters. The code is made publicly available at: \href{https://github.com/ayanban011/SwinDocSegmenter}{github.com/ayanban011/SwinDocSegmenter}