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const main = async () => {
const response = await fetch('https://api.ai.cc/v1/ocr', {
method: 'POST',
headers: {
Authorization: 'Bearer ',
'Content-Type': 'application/json',
},
body: JSON.stringify({
document: {
type: 'document_url',
document_url: 'https://css4.pub/2015/textbook/somatosensory.pdf'
},
model: 'mistral/mistral-ocr-latest',
}),
}).then((res) => res.json());
console.log(response);
};
main();
import requests
def main():
response = requests.post(
"https://api.ai.cc/v1/ocr",
headers={
"Authorization": "Bearer ",
"Content-Type": "application/json",
},
json={
"document": {
"type": "document_url",
"document_url": "https://css4.pub/2015/textbook/somatosensory.pdf"
},
"model": "mistral/mistral-ocr-latest",
},
)
response.raise_for_status()
data = response.json()
print(data)
if __name__ == "__main__":
main()
-
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Product Detail
Mistral OCR, developed by Mistral AI, represents a leap forward in Optical Character Recognition (OCR) technology. This advanced API is meticulously engineered for superior document understanding, capable of processing a wide array of formats including PDFs, images, and scanned documents. It excels at extracting text, intricate tables, complex equations, and even images with remarkable accuracy, all while faithfully preserving the original document's structure and layout.
✨ Core Capabilities of Mistral OCR
High-Accuracy Text Extraction: Achieving an impressive 94.89% overall accuracy, Mistral OCR surpasses many competitors. It reliably extracts text from scanned documents, handwritten notes, and diverse multilingual content, providing dependable data for subsequent applications and analyses.
Multimodal Document Understanding: This API efficiently processes both PDFs and images, intelligently recognizing and preserving the context and relationships of interleaved elements such as images, tables, charts, and mathematical equations. Outputs are delivered in structured Markdown or JSON formats, ready for AI workflows.
Extensive Multilingual Proficiency: With support for thousands of languages and an outstanding 99.02% fuzzy match accuracy, Mistral OCR is an invaluable tool for global enterprises. It effortlessly handles diverse document sets, from Hindi to Chinese, ensuring seamless global operations.
Structured Output & Layout Preservation: Mistral OCR meticulously retains the original document's hierarchy, including headers, paragraphs, lists, and tables. This ensures outputs are AI-ready, facilitating integration with Retrieval-Augmented Generation (RAG) systems, efficient search indexing, and automated workflows.
Doc-as-Prompt Functionality: Empowering users to directly query specific document content or extract structured data through AI-driven prompts, this feature significantly enhances precision in information retrieval and analytical tasks.
High-Speed Processing: Optimized for large-scale document repositories, Mistral OCR can process up to 2000 pages per minute. This dramatically reduces processing times for enterprises, research institutions, and any organization dealing with high volumes of documents.
Self-Hosting for Data Privacy: For organizations with stringent security and compliance requirements, Mistral OCR offers on-premises deployment options, ensuring sensitive data remains securely within their private infrastructure.
⚙️ Technical Specifications & Benchmarks
Mistral OCR's robust performance stems from its transformer-based architecture, featuring specialized attention mechanisms for deep context and layout understanding. It supports multimodal inputs (PDFs, images) and delivers structured outputs (Markdown, JSON) tailored for RAG systems.
Key Performance Highlights:
- ✅ Context Window: Processes up to 1000 pages per request.
- ⚡️ Processing Speed: Handles up to 2000 pages per minute on a single node.
- 💰 API Pricing: Highly competitive at $0.00105 per page.
- ⚠️ Limitations: Maximum file size of 50 MB and maximum page count of 1000 pages per request.
Accuracy Benchmarks:
- 📊 Overall Accuracy: 94.89% (outperforms Google Document AI, Azure OCR, GPT-4o)
- ➗ Mathematical Expressions: 94.29%
- 🌍 Multilingual Text: 89.55%
- 📄 Scanned Documents: 98.96%
- 🔠 Table Recognition: 96.12%
Mistral OCR metrics in comparison
💡 Optimal Use Cases for Mistral OCR
- 🔬 Research & Academia: Efficiently digitize scientific papers, including complex equations and charts, into AI-ready formats for advanced analysis.
- 💼 Business & Finance: Automate the processing of invoices, contracts, and financial reports for structured data extraction and rapid insights.
- ⚖️ Legal & Compliance: Convert legal filings and records into easily searchable, indexed digital formats, streamlining compliance and discovery.
- 📚 Education: Transform lecture notes, textbooks, and educational materials into accessible digital content for students and educators.
- 📞 Customer Service: Index user manuals and support documents to significantly reduce response times and enhance overall customer satisfaction.
🆚 Mistral OCR: A Competitive Edge
Mistral OCR consistently demonstrates superior document understanding capabilities when compared to both traditional and other AI-based OCR solutions:
- vs. Gemini 2.5 Flash: Mistral OCR boasts superior OCR accuracy (94.89% vs. ~88.49%) and table recognition, though Gemini offers broader general multimodal reasoning.
- vs. Google Document AI: Achieves higher accuracy in mathematical expressions (94.29% vs. ~90%) and multilingual text (89.55% vs. ~85%). It also offers faster processing (2000 vs. ~1000 pages/min).
- vs. Azure OCR: Provides better layout preservation and more structured outputs, although Azure typically offers more extensive enterprise integrations.
- vs. GPT-4o: Outperforms in handling scanned documents (98.96% vs. ~95%) and complex equations. However, GPT-4o offers greater versatility for tasks beyond core OCR.
⚠️ Important Considerations & Limitations
- Hallucinations Risk: Mistral OCR may occasionally infer missing or unclear text, which could lead to errors in critical applications such as legal or financial document processing.
- No Built-in Document Classification: Additional systems are required for organizing and categorizing extracted data, as this is not an inherent feature of the API.
- Text Misclassification: In some instances, entire pages might be erroneously treated as images, potentially resulting in incomplete text extraction.
- File Constraints: The API has specific limits, processing files up to a maximum of 50 MB and 1000 pages per individual request.
🔗 Seamless API Integration
Mistral OCR is readily accessible via the AI/ML API, offering comprehensive support for popular programming languages including Python, JavaScript, and cURL. It delivers structured outputs in JSON or Markdown formats, ensuring easy integration into existing workflows.
For detailed setup instructions and usage examples, refer to the official Mistral OCR API Documentation.
❓ Frequently Asked Questions (FAQs)
Q1: What types of documents can Mistral OCR process?
A1: Mistral OCR can process a wide range of documents including PDFs, various image formats, and scanned documents, accurately extracting text, tables, equations, and images.
Q2: How accurate is Mistral OCR compared to other solutions?
A2: Mistral OCR achieves an overall accuracy of 94.89%, outperforming major competitors like Google Document AI, Azure OCR, and GPT-4o in several key areas such as math, multilingual text, and scanned document recognition.
Q3: Can Mistral OCR handle multiple languages?
A3: Yes, it supports thousands of languages with a 99.02% fuzzy match accuracy, making it highly effective for global applications and diverse document sets.
Q4: What are the main limitations of Mistral OCR?
A4: Key limitations include potential hallucinations (guessing unclear text), lack of built-in document classification, occasional text misclassification as images, and file constraints of 50 MB and 1000 pages per request.
Q5: Is self-hosting an option for Mistral OCR?
A5: Yes, Mistral OCR offers on-premises deployment options, ideal for organizations with strict data privacy and security requirements, allowing sensitive data to remain within their private infrastructure.
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