datalab-to/chandra
chandra
OCR model that handles complex tables, forms, handwriting with full layout.
Usage guide
chandra is an open-source project around ocr with 11,358 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating chandra for Python AI workflows.
- Comparing a GitHub project with 11,358 stars and current repository activity.
Pros
- chandra has visible GitHub traction with 11,358 stars. Topics: ai, ocr.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
chandra should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
chandra architecture preview
chandra's main path starts at the entry surface, runs through chandra core runtime, combines Optional AI model, Files / repository context, GitHub, and returns User-facing result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://www.datalab.to
Runtime
chandra core runtime
The core coordinates project logic, configuration, and AI-related execution in Python.
Python
Model
Optional AI model
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Files / repository context
Context comes from Files / repository context, which constrains what the model or runtime can use.
Files / repository context
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
User-facing result
The final output is returned to the user, workflow, API caller, or downstream system.
output
Featured video
1littlecoder
Chandra OCR in 9 mins!
18,496 views ยท 2025-11-01
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
chandra depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/datalab-to/chandra.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ pip install chandra-ocrAdoption guidance and sources
Practical use cases
OCR model that handles complex tables, forms, handwriting with full la
This is one of the documented reasons to evaluate chandra before choosing a stack.
Focus area: ai
This is one of the documented reasons to evaluate chandra before choosing a stack.
All project comparison
Compare chandra with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official chandra setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented chandra example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is chandra?
chandra is an open-source all project. OCR model that handles complex tables, forms, handwriting with full layout.
How do I install chandra?
Start with the official README. The first detected setup step is: git clone https://github.com/datalab-to/chandra.git.
Is chandra beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can chandra be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does chandra need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt chandra?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.