Chandra OCR Guide: Installation, Benchmarking, and Advanced Use Cases

Part 1. What is Chandra OCR?

Chandra OCR is an advanced Optical Character Recognition (OCR) model built for structured document understanding.

Unlike traditional OCR systems that only recognize plain text, Chandra OCR is designed to understand complex document layouts — including tables, mathematical formulas, handwriting, and multi-column pages.

Developed by the team at DataLab, this model has quickly gained attention on Hugging Face and GitHub for its impressive accuracy and multi-language support.

Why Chandra OCR Matters

Most existing OCR tools — such as Tesseract, PaddleOCR, or commercial APIs like Google Vision — focus on text detection.

However, Chandra OCR aims to recreate the document structure, not just the text content. It can output recognized data in Markdown, HTML, or JSON, preserving headings, tables, and image positions.

This makes it particularly valuable for research papers, invoices, scanned books, and academic materials.

Related topic:

What Is OCR? Text Recognition Basics & Tools [2025 Guide]

Part 2. How Accurate Is Chandra OCR?

When evaluating an OCR tool, marketing claims are meaningless without hard data. The true measure of performance lies in rigorous, independent benchmarking. For Chandra OCR, the results are not just impressive; they signal a fundamental shift in what's possible for document understanding.

This analysis is based on the authoritative olmocr benchmark, a respected standard for evaluating OCR performance, providing a clear and unbiased comparison against the industry's leading models.

2. Detailed Task Analysis: Unveiling Technical Breakthroughs

Chandra's true prowess is revealed in its performance on specialized, challenging tasks where most models struggle.

Please swipe to view

Model

ArXiv

Old Scans Math

Tables

Old Scans

Headers & Footers

Multi-column

Long Tiny Text

Overall

Datalab Chandra v0.1.0

82.2

80.3

88.0

50.4

90.8

81.2

92.3

83.1 ± 0.9

Datalab Marker v1.10.0

83.8

69.7

74.8

32.3

86.6

79.4

85.7

76.5 ± 1.0

Mistral OCR API

77.2

67.5

60.6

29.3

93.6

71.3

77.1

72.0 ± 1.1

• Mathematical Formulas in Old Scans: With a score of 80.3%, Chandra leads the second-place model by a remarkable 5.4 points. This demonstrates an exceptional ability to parse complex notation even in low-quality, historical scans.
• Table Recognition: Scoring 88.0%, Chandra approaches near-perfect accuracy in reconstructing complex table structures, a critical feature for financial and scientific document processing.
• Long-Form Tiny Text: Achieving a dominant 92.3%, Chandra excels at accurately reading dense, small-font text, outperforming alternatives by a substantial margin.

Part 3. How to Get Started with Chandra OCR

Let's move from theory to practice. This section provides a foolproof guide to installing Chandra OCR and using it for common tasks.

1. Install Chandra OCR

Important: The method shown in the provided image is incorrect. The only recommended method is installation via PyPI.

Step 1: Create a Virtual Environment

This prevents conflicts between Python packages.

Code

# Create the virtual environment
python -m venv chandra-env
# Activate the virtual environment
# Linux/macOS:
source chandra-env/bin/activate
# Windows Command Prompt:
chandra-env\Scripts\activate.bat
# Windows PowerShell:
chandra-env\Scripts\Activate.ps1

Step 2: Install Chandra OCR using pip

Code

pip install chandra-ocr

Step 3: Verify the installatio

Code

chandra_ocr --version

2. Using Chandra OCR from the Command Line

The command chandra-ocr process --input ... shown in the image is incorrect and will not work. The accurate command structure is simpler.

Step 1. Process a single file (e.g., a PDF or image):

Code

chandra_ocr path/to/your/document.pdf

The extracted text will be printed directly to the terminal.

Step 2. Process a file and save the output to a file:

Code

chandra_ocr path/to/your/document.pdf -o ./my_output.txt
# Or using the long form:
chandra_ocr path/to/your/document.pdf --output ./my_output.txt

Step 3. Batch process all files in a directory:

Code

chandra_ocr ./path/to/input/documents/ -o ./path/to/output/folder/

This command will process all supported files in the documentsfolder and save each result as a separate text file in the outputfolder.

Step 4. Specify a language (e.g., for a document in Simplified Chinese):

Code

# For Simplified Chinese:
chandra_ocr -l chi_sim my_document.pdf
# For multilingual documents (English + German):
chandra_ocr -l eng+deu my_document.pdf

Note: The language code (e.g., chi_sim) must correspond to a Tesseract language pack installed on your system.

Part 4: Chandra OCR vs. Other OCR Tools: A Detailed Comparison

Chandra OCR stands out when compared to popular OCR solutions like Tesseract, Adobe OCR, and Google Cloud Vision.

Please swipe to view

To learn more about popular OCR tools and their comparisons, also read: 10 Best Free OCR Software in 2025: Expert Tested & Reviewed.

Our Top Recommendation: Tenorshare PDNob

However, for most users seeking a powerful, all-in-one, and user-friendly solution, we most highly recommend Tenorshare PDNob.

While Chandra OCR excels in technical and automated environments, Tenorshare PDNob delivers an unmatched user experience by combining high recognition accuracy with exceptional layout preservation in an intuitive interface.

It provides reliable batch processing capabilities without the complexity of command-line tools, making it the ideal choice for professionals, students, and businesses who prioritize efficiency, ease of use, and consistently excellent results for their everyday document digitization needs.

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 5. Advanced Use Cases: Integrating Chandra OCR with AI Pipelines

This is where Chandra OCR transforms from a simple tool into a powerhouse. It excels as the "data acquisition" layer in a larger AI system.

Use Case: Building an Intelligent Document Analysis Pipeline

Step 1: Text Extraction with Chandra OCR

We use Chandra OCR to efficiently convert PDFs to text. This can be scripted easily.

Code

# Create the output directory if it doesn't exist
mkdir -p ./text_output

# Batch convert all PDFs in a folder to text files
for pdf in ./reports/*.pdf; do
    # Use basename to create a corresponding .txt file for each PDF
    output_file="./text_output/$(basename "$pdf" .pdf).txt"
    chandra_ocr "$pdf" -o "$output_file"
done

Step 2: Send Text to an AI Model (e.g., on Hugging Face)​

Using a simple Python script, we can take the output from Chandra OCR and send it to an AI model.

Code

from transformers import pipeline
import os

# 1. Initialize the summarization pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# 2. Define the path to the text file extracted by Chandra OCR
input_file_path = "./text_output/report_123.txt"

try:
    # 3. Safely read the file with error handling
    with open(input_file_path, 'r', encoding='utf-8') as file:
        extracted_text = file.read()

    # 4. Handle long documents by chunking them logically
    # This is a simplistic chunker. For production, use a text splitter library.
    max_chunk_length = 1024
    text_chunks = [extracted_text[i:i+max_chunk_length] for i in range(0, len(extracted_text), max_chunk_length)]

    summaries = []
    for chunk in text_chunks:
        # Summarize each chunk
        summary_result = summarizer(chunk, max_length=150, min_length=40, do_sample=False)
        summaries.append(summary_result[0]['summary_text'])

    # 5. Combine the summaries
    final_summary = " ".join(summaries)

    # 6. Output the final summary
    print("Document Summary:")
    print(final_summary)

except FileNotFoundError:
    print(f"Error: The file '{input_file_path}' was not found. Please check the path.")
except Exception as e:
    print(f"An unexpected error occurred: {e}")

Part 6. Chandra OCR FAQ: Common Questions and Issues

Speak Your Mind

Speak Your Mind

Related articles

• 10 Best Free OCR Software in 2025: Expert Tested & Reviewed​
• [2025 Update] 7 Best OCR Recognition Software for Text and Handwriting
• How to Make a PDF Searchable - Online/Free/Without Acrobat

All topics