olmOCR: How to Use olmOCR via GitHub, API, and Hugging Face

Part 1. What Is olmOCR and Why It Matters

olmOCR (Open Language Model for OCR) is a state-of-the-art AI OCR system combining vision-language models and layout-aware parsing. Unlike traditional OCR tools such as Tesseract, olmOCR can:

• Analyze text in the context of document layout (columns, tables, figures)
• Recognize handwritten content
• Process multi-page documents with complex formatting

Technical Highlights:

• Architecture: Vision-Language Transformer + Layout-aware modules
• Input Types: PDFs, scanned images, TIFF, JPEG
• Output Types: Structured JSON, OCR PDF
• Open Source: GitHub Repository
• Hugging Face Models: olmOCR Hugging Face Collection

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By integrating semantic understanding with layout parsing, olmOCR enables researchers and developers to extract information from complex PDFs, including academic papers, financial reports, and legal documents.

Related topic:

What Is OCR? Text Recognition Basics & Tools

Part 2. How to Use olmOCR (Practical Setup for Developers)

olmOCR is designed for developers, researchers, and tech enthusiasts. It provides multiple deployment options, including GitHub cloning, Python API, Docker containers, and Hugging Face models. Here’s a step-by-step guide to using olmOCR effectively.

2.1 GitHub Cloning and Local Installation

• Clone the repository:
  

  Code

  git clone https://github.com/allenai/olmocr.git
  cd olmocr

  Keyword: olmocr github. Make sure Git is installed on your system (Windows/macOS/Linux).

• Install dependencies:
  

  Code

  pip install -r requirements.txt

  This installs Python packages such as PyTorch, Transformers, and OpenCV required for running olmOCR.

• Download pre-trained models:
  

  Code

  python -m olmocr.download_model --model base

  Base model is suitable for quick testing; Large model is recommended for high-accuracy OCR.

• Run a sample OCR task:
  

  Code

  python -m olmocr.run --input sample.pdf --output result.json

  Output: result.json contains extracted text and layout information.

2.2 Python API Usage

olmOCR provides Python API for integrating OCR into your workflows:

Code

from olmocr import OCRModel

# Load the pre-trained model
model = OCRModel.load("allenai/olmocr-base")

# Recognize text in a PDF
result = model.recognize("invoice.pdf")

# Access text and layout
print(result.text)
print(result.layout)

Supports PDFs, PNGs, JPEGs. Outputs structured JSON.

Batch Processing Example

Code

import os
from olmocr import OCRModel

model = OCRModel.load("allenai/olmocr-base")
input_dir = "pdf_folder/"
output_dir = "ocr_results/"

for file in os.listdir(input_dir):
    if file.endswith(".pdf"):
        pdf_path = os.path.join(input_dir, file)
        result = model.recognize(pdf_path)
        output_path = os.path.join(output_dir, file.replace(".pdf", ".json"))
        result.save_json(output_path)

Ideal for processing multiple academic papers, invoices, or reports.

2.3 Docker Deployment

1. Pull the Docker image:
   

   Code

   docker pull allenai/olmocr

2. Run OCR on a PDF:
   

   Code

   docker run -v $(pwd):/data allenai/olmocr \
   --input /data/sample.pdf \
   --output /data/result.json

   Output result.json is saved locally. Keyword: olmocr docker.

3. Batch Processing with Docker:
   

   Code

   for f in ./pdf_folder/*.pdf; do
       docker run -v $(pwd):/data allenai/olmocr --input /data/$f --output /data/out_$f.json
   done

2.4 Hugging Face Model Deployment

Code

from transformers import AutoProcessor, AutoModelForSeq2SeqLM
from PIL import Image

processor = AutoProcessor.from_pretrained("allenai/olmocr-base")
model = AutoModelForSeq2SeqLM.from_pretrained("allenai/olmocr-base")

image = Image.open("scan.png")
inputs = processor(images=image, return_tensors="pt")
outputs = model.generate(**inputs)
text = processor.batch_decode(outputs, skip_special_tokens=True)
print(text)

2.5 Tips for Optimal OCR Performance

• High-resolution scans: ≥300 DPI for best results.
• Handwritten documents: Use olmOCR 2 for improved handwriting recognition.
• Multi-page/multi-column documents: Parse JSON output for structured data.
• GPU acceleration: Combine with Ollama or vLLM for faster inference.

Following these steps, developers can deploy olmOCR locally, on servers, or in the cloud, achieving high-accuracy OCR for academic research, enterprise workflows, or custom development pipelines.

For non-developers who want a simple PDF OCR solution, check out Tenorshare PDNob, which delivers fast, accurate, and user-friendly OCR without requiring coding skills.

downloads :

PDNob PDF Editor Software- Smarter, Faster, Easier

rated on Trustpilot >

• Instantly read, summarize, and extract insights from PDF
• Convert PDF to 30+ formats like Word, Excel, and images
• Edit text, images, watermarks, links, and backgrounds for PDF
• 99% OCR precision for making scanned PDFs editable and searchable

Part 3. olmOCR Performance Comparison

To help users quickly understand olmOCR’s advantages, here’s a clear comparison with other popular OCR tools. The focus is on accuracy, speed, batch processing, and integration.

Performance Comparison Table

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Related topic:

10 Best Free OCR Software in 2025: Expert Tested & Reviewed​

Key Insights:

• olmOCR provides top-tier accuracy for both printed and handwritten documents, outperforming many open-source options
• Batch processing with GPU support makes it suitable for large-scale or enterprise workflows
• Flexible integration via Python API, Docker, or Hugging Face allows developers to easily incorporate it into custom pipelines
• While commercial OCR tools may offer GUI convenience, olmOCR balances performance, flexibility, and developer control

Part 4: olmOCR 2: New Capabilities in 2025

olmOCR 2 brings faster performance, higher accuracy, and better integration, addressing the needs of developers, researchers, and businesses.

• Improved Accuracy: 98–99% for printed text, 85–90% for handwriting. Works well with multi-column PDFs and tables. (olmocr 2, ai ocr)
• Faster Processing: Optimized for GPU and batch workflows, ideal for large document collections. (olmocr docker, pdf to ocr pdf)
• Flexible Integration: Enhanced Python API, Docker deployment, and Hugging Face support make it easy to integrate into pipelines. (olmocr python, olmocr huggingface, olmocr api)
• Output Options: Convert scanned PDFs to searchable PDFs, JSON, or plain text. (ocr converter, pdf to ocr pdf)
• Developer & Research Ready: Handles complex layouts and scanned academic papers efficiently. (how to use olmocr, olmocr github, olmocr paper)

Part 5. olmOCR vs AI OCR PDF Editor

olmOCR is an excellent AI OCR tool for developers and researchers who are comfortable with Python, Docker, or Hugging Face. It excels at extracting text from complex scanned documents and academic papers. However, for many everyday users—students, professionals, and businesses—setting up and managing olmOCR can be challenging.

This is where Tenorshare PDNob stands out as a practical, user-friendly alternative. Unlike olmOCR, PDNob PDF Editor combines advanced AI OCR with a full-featured PDF editor, letting you edit, annotate, convert, and secure your PDFs all in one place. Users don’t need any programming knowledge—just open the file, run OCR, and start editing.

Key Advantages of Tenorshare PDNob PDF Editor

• All-in-One Solution: Extract text, edit content, annotate, merge, split, and sign PDFs.
• High OCR Accuracy: Advanced AI handles printed and handwritten text with precision, making it ideal for business documents, academic papers, or invoices.
• Batch Processing & Speed: Process multiple files at once, saving time for large projects—no GPU setup required.
• Flexible Output Options: Convert scanned PDFs to searchable PDFs, Word, Excel, or plain text seamlessly.
• Secure & Reliable: Encrypt files, add digital signatures, and track edits.

How to Use Tenorshare PDNob PDF Editor

• Open the PDNob PDF Editor,then click on “Open PDF” button to import any PDF file you’ve downloaded.

• Click the “Edit” button on the top toolbar, then select the text you want to modify. A text box will appear, allowing you to change the text, font, style, size, and color.

• To add new text, click “Add Text” under the “Edit” section and place it where needed.
• To insert images, click the “Add Image” button and choose the image file to add.

• Once you have made all the necessary edits, click on the "Save" button in the top left corner.

Part 6. FAQs of olmOCR

Q1: Can olmOCR convert scanned PDFs into editable PDFs?

Yes. olmOCR can create searchable PDFs, extract plain text, or export structured JSON. However, it does not include a built-in PDF editor, so further editing requires additional software like PDNob PDF Editor.

Q2: How accurate is olmOCR?

Printed text: 98–99% accuracy
Handwriting: 85–90% accuracy
Multi-column or complex layouts: reliable extraction
Accuracy may vary depending on scan quality and language.

Q3: How can I use olmOCR if I’m not a developer?

olmOCR is primarily developer-focused, requiring Python or Docker setup. For non-technical users, GUI-based alternatives like PDNob PDF Editor AI OCR allow easy OCR processing, PDF editing, and batch conversion without coding.

Q4: Does olmOCR support multiple languages?

Yes. olmOCR 2 supports multiple languages and character sets, including English, Chinese, Spanish, and more. Users can integrate custom models for additional languages via Hugging Face.

Q5: What file formats does olmOCR support?

Input formats: PDF, PNG, JPEG, TIFF.
Output formats: searchable PDF, plain text, and structured JSON. This makes it useful for research, business documents, and academic papers.

Q6: Is there a cloud or online version of olmOCR?

Currently, olmOCR runs locally via Python or Docker. Online testing is available via Hugging Face demos, but bulk document processing is best handled locally.

Q7: How does olmOCR compare to commercial OCR tools?

olmOCR excels in accuracy and flexibility for developers but lacks GUI and built-in editing. Tools like PDNob PDF Editor AI OCR combine advanced AI OCR with user-friendly PDF editing, batch processing, and export options, making them ideal for professionals or students.

Q8: Where can I find olmOCR code or models?

olmOCR is open-source and available on GitHub (olmOCR GitHub) and Hugging Face (olmOCR Hugging Face), including Docker images and Python APIs for integration.

Conclusion

olmOCR 2 is a powerful AI OCR tool for developers and researchers, offering high accuracy for printed and handwritten text. For non-technical users or those needing a complete PDF solution, PDNob PDF Editor provides an easy-to-use alternative with advanced OCR, full PDF editing, batch processing, and flexible output options.

downloads :

PDNob PDF Editor Software- Smarter, Faster, Easier

rated on Trustpilot >

• Instantly read, summarize, and extract insights from PDF
• Convert PDF to 30+ formats like Word, Excel, and images
• Edit text, images, watermarks, links, and backgrounds for PDF
• 99% OCR precision for making scanned PDFs editable and searchable

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