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DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond

Cong Yao

TL;DR

DocXChain tackles the challenge of making unstructured documents machine-accessible by providing an open-source, modular toolchain that jointly performs text detection, text recognition, layout analysis, and table structure recognition. It offers ready-to-run pipelines (general text reading, table parsing, and document structurization) built from these atomic capabilities and designed to integrate with LangChain and ChatGPT. The work emphasizes real-world robustness and interoperability, noting limitations of closed-source solutions like GPT-4V Vision and positioning DocXChain as a lightweight, open-source alternative. The authors release both models and code to enable broad adoption and outline future directions to combine DocXChain with LLMs for information extraction, QA, and retrieval-augmented generation. The impact is enabling scalable digitization and automated document understanding across diverse formats and languages.

Abstract

In this report, we introduce DocXChain, a powerful open-source toolchain for document parsing, which is designed and developed to automatically convert the rich information embodied in unstructured documents, such as text, tables and charts, into structured representations that are readable and manipulable by machines. Specifically, basic capabilities, including text detection, text recognition, table structure recognition and layout analysis, are provided. Upon these basic capabilities, we also build a set of fully functional pipelines for document parsing, i.e., general text reading, table parsing, and document structurization, to drive various applications related to documents in real-world scenarios. Moreover, DocXChain is concise, modularized and flexible, such that it can be readily integrated with existing tools, libraries or models (such as LangChain and ChatGPT), to construct more powerful systems that can accomplish more complicated and challenging tasks. The code of DocXChain is publicly available at:~\url{https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/Applications/DocXChain}

DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond

TL;DR

DocXChain tackles the challenge of making unstructured documents machine-accessible by providing an open-source, modular toolchain that jointly performs text detection, text recognition, layout analysis, and table structure recognition. It offers ready-to-run pipelines (general text reading, table parsing, and document structurization) built from these atomic capabilities and designed to integrate with LangChain and ChatGPT. The work emphasizes real-world robustness and interoperability, noting limitations of closed-source solutions like GPT-4V Vision and positioning DocXChain as a lightweight, open-source alternative. The authors release both models and code to enable broad adoption and outline future directions to combine DocXChain with LLMs for information extraction, QA, and retrieval-augmented generation. The impact is enabling scalable digitization and automated document understanding across diverse formats and languages.

Abstract

In this report, we introduce DocXChain, a powerful open-source toolchain for document parsing, which is designed and developed to automatically convert the rich information embodied in unstructured documents, such as text, tables and charts, into structured representations that are readable and manipulable by machines. Specifically, basic capabilities, including text detection, text recognition, table structure recognition and layout analysis, are provided. Upon these basic capabilities, we also build a set of fully functional pipelines for document parsing, i.e., general text reading, table parsing, and document structurization, to drive various applications related to documents in real-world scenarios. Moreover, DocXChain is concise, modularized and flexible, such that it can be readily integrated with existing tools, libraries or models (such as LangChain and ChatGPT), to construct more powerful systems that can accomplish more complicated and challenging tasks. The code of DocXChain is publicly available at:~\url{https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/Applications/DocXChain}
Paper Structure (7 sections, 4 figures, 2 tables)

This paper contains 7 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Design and Implementation of DocXChain
  3. Core Ideology
  4. System Overview
  5. Modules and Pipelines
  6. Qualitative Examples
  7. Conclusion and Outlook

Figures (4)

  • Figure 1: System overview of DocXChain.
  • Figure 2: General text reading example. The text detections are represented with orange quadrangles, while the text contents are listed on the right panel.
  • Figure 3: Table parsing example. The original image is shown on the left, while the table cells (in green) and text detections (in orange) are depicted on the right. For clarity, the recognized text contents are not overlaid on the image, but listed in the box below.
  • Figure 4: Document structurization example. Different colors are used to illustrate the categories of different layout regions. The text detections are represented with orange quadrangles. For clarity, the recognized text contents are skipped.