OCR Benchmark
Multimodal benchmark comparing the OCR and JSON extraction performance of various LLM models, including Gpt-4o and Gemini 2.0.
Description
The dataset "OCR Benchmark" is a comprehensive assessment body designed to measure the OCR and JSON data extraction capabilities of advanced multimodal models. It contains 1,000 annotated examples that can be used to compare systems like Gpt-4o and Gemini 2.0.
What is this dataset for?
- Evaluate the OCR accuracy of multimodal models
- Compare the quality of extraction of structured data (JSON) by different LLMs
- Test and improve combined visual and text comprehension skills
💡 Want to learn more on OCR? Discover our article: Importance of OCR in the AI era
Can it be enriched or improved?
This benchmark can be extended with more examples or other types of documents to better cover real use cases. Adding additional quality or error annotations could also be beneficial.
🔎 In summary
🧠 Recommended for
- OCR researchers
- LLM multimodal developers
- QA engineers
🔧 Compatible tools
- Hugging Face Datasets
- Pandas
- OCR assessment tools
- Multimodal frameworks
💡 Tip
Use this benchmark to validate OCR robustness on various documents before deployment.
Frequently Asked Questions
Does this dataset contain documents in multiple languages?
Mostly in English, but it is possible to extend it with other languages for multilingual tests.
What is the size of the dataset and what format is it in?
Approximately 386 MB, available in JSON and Parquet formats, with 1,000 examples.
Can this dataset be used to train an OCR model?
Yes, it can be used for fine-tuning, especially to improve multimodal extraction of text and structured data.
