Author(s): Florian June

Originally published on Towards AI.

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A large number of documents — including technical documentation, historical records, academic publications, and legal files — exist in scanned or image formats. This presents significant challenges for downstream tasks like Retrieval-Augmented Generation (RAG), information extraction, and document understanding.

Document parsing addresses these challenges by identifying and extracting various elements like text, equations, tables, and images from diverse documents while preserving their structural relationships. The extracted content is then converted into structured formats such as Markdown, HTML, or JSON, enabling seamless integration with downstream tasks.

In previous articles, we have shared numerous technologies related to intelligent document parsing. This article reviews and summarizes these technologies from my previous writings and two novel surveys, concluding with my personal thoughts and insights.

Figure 1: Overview of document parsing methodology. Source: Document Parsing Unveiled.

Document parsing can be broadly categorized into two methodologies: the modular pipeline system and the end-to-end approach based on large vision-language models.

Figure 2: Two methodology of document parsing. Source: Document Parsing Unveiled.

Modular pipeline system breaks down document parsing into separate stages, each focusing on specific tasks and features. The modules are typically as follows:

Layout Analysis: Detects document structure by identifying elements like… Read the full blog for free on Medium.

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