Demystifying PDF Parsing 05: Unifying Separate Tasks into a Small Model

Author(s): Florian June

Originally published on Towards AI.

Mechanics, Code, Insights on GOT, DLAFormer, and UNIT

This member-only story is on us. Upgrade to access all of Medium.

This article is the fifth in the series. The previous articles introduced several mainstream solutions for PDF parsing and document intelligence, including:

Categorizing the main tasks of PDF parsing and providing brief introductions to each.Pipeline-based methods.OCR-free small model-based methods.OCR-free large multimodal model-based methods.

In this article, we explore the latest advancements in this field, with a focus on unifying separate sub-tasks into a small model (less than 1B parameters).

We begin by reviewing the previous content from the series and providing a brief overview of unified small model. Next, we introduce three approaches to achieving unification. Finally, we share insights and key takeaways.

Let’s first review the previous methods.

Pipeline-based methods used modular architectures, where tasks like text recognition, layout detection, and table understanding were handled separately. Although functional, these systems often led to high maintenance costs and limited generalization, because different tasks required separate models.

Figure 1: Overview of pipeline-based method. Image by author.

OCR-free small model-based methods are effective in specific areas such as academic paper or formula recognition; however, their applicability is limited due to architectural constraints and task-specific designs.

Figure 2: OCR-free small model-based method. Image by author.

OCR-free large multimodal model-based methods are… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI