SynthDoc: Bilingual Documents Synthesis for Visual Document Understanding

TL;DR

SynthDoc is a new pipeline for generating diverse synthetic bilingual document datasets that improve visual document understanding models' performance and robustness.

Contribution

It introduces a scalable synthetic data generation method for VDU, enhancing dataset diversity and supporting end-to-end document parsing research.

Findings

Models trained with SynthDoc data outperform baselines in pre-training tasks.

SynthDoc-generated datasets improve robustness in downstream VDU tasks.

A benchmark of 5,000 image-text pairs is released for community use.

Abstract

This paper introduces SynthDoc, a novel synthetic document generation pipeline designed to enhance Visual Document Understanding (VDU) by generating high-quality, diverse datasets that include text, images, tables, and charts. Addressing the challenges of data acquisition and the limitations of existing datasets, SynthDoc leverages publicly available corpora and advanced rendering tools to create a comprehensive and versatile dataset. Our experiments, conducted using the Donut model, demonstrate that models trained with SynthDoc's data achieve superior performance in pre-training read tasks and maintain robustness in downstream tasks, despite language inconsistencies. The release of a benchmark dataset comprising 5,000 image-text pairs not only showcases the pipeline's capabilities but also provides a valuable resource for the VDU community to advance research and development in document image recognition. This work significantly contributes to the field by offering a scalable solution to data scarcity and by validating the efficacy of end-to-end models in parsing complex, real-world documents.