Reading
OpenLLM OCR Annotator is a multimodal OCR annotation framework that supports multiple mainstream large model APIs. It can automatically extract structured text information from images and export it in various formats, significantly reducing manual annotation costs.
Section 01
OpenLLM OCR Annotator is a multimodal OCR annotation framework that supports multiple mainstream large model APIs. It can automatically extract structured text information from images and export it in various formats, significantly reducing manual annotation costs.
Section 02
Section 03
In the intersection of computer vision and natural language processing, Optical Character Recognition (OCR) has always been a fundamental and key technology. However, traditional OCR annotation processes face many challenges:
First, manual annotation costs are high. For complex document images, annotators need to carefully read each line of text, identify table structures, and extract key fields—this process is both time-consuming and error-prone.
Second, processing multi-language and multi-format documents is difficult. Documents such as foreign trade invoices, receipts, and contracts often contain mixed languages, handwritten and printed text, which traditional OCR tools struggle to recognize accurately.
Third, annotation formats are not unified. Different machine learning frameworks and training tasks require different data formats (JSON, YAML, COCO, TSV, etc.), and manual conversion is both tedious and error-prone.
It is these pain points that gave birth to intelligent annotation tools like OpenLLM OCR Annotator.
Section 04
OpenLLM OCR Annotator is an open-source multimodal OCR annotation framework. Its core innovation lies in using the visual understanding capabilities of Large Language Models (LLMs) to automate the document annotation process. Unlike traditional OCR solutions based on rules or pure computer vision models, this project fully leverages the powerful capabilities of multimodal large models in image understanding, text extraction, and structured output.
This project was created and maintained by developer Loong Ma, uses the MIT open-source license, and its code is hosted on GitHub. The project is designed to be concise with minimal dependencies, developed using Python 3.13.2, and recommends using uv for environment management.
Section 05
The biggest highlight of OpenLLM OCR Annotator is its wide model compatibility. The framework natively supports multiple mainstream large model APIs:
This multi-model support strategy has important value: users can flexibly choose the backend model based on data privacy requirements, cost budgets, and performance needs. For example, when processing sensitive documents, you can use the locally deployed Qwen model, while for the highest accuracy, you can call GPT-4 Vision.
Section 06
The framework supports joint input of images and text, which allows annotation tasks to obtain richer contextual information. For example, when annotating foreign trade documents, the system can receive both the document image and relevant prompt instructions at the same time, thereby understanding the document structure and field meanings more accurately.
Section 07
The project supports multiple annotation output formats, including:
Upcoming supported formats include TSV, XML, and CSV, which will further improve compatibility with various ML frameworks.
Section 08
The framework provides field-level and document-level accuracy evaluation functions. Through streamlit_viewer.py, users can intuitively view annotation results, verify the accuracy of model outputs, and adjust prompt templates or switch models accordingly.
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