Chandra is an OCR model that handles complex tables, forms, and handwriting while preserving full document layout.
The tool addresses the challenge of extracting text from documents where structure matters as much as content. Traditional OCR systems often lose spatial relationships and formatting when converting images to text. Chandra takes a different approach by maintaining the complete layout information alongside the recognized text, making it suitable for documents where the arrangement of elements carries semantic meaning.
Organizations processing structured documents like forms, invoices, or data tables should consider Chandra when layout preservation is critical to downstream processing. The model's ability to handle handwriting alongside printed text and complex table structures distinguishes it from basic OCR solutions that treat documents as linear text streams. This makes it particularly valuable for workflows where document structure needs to feed into automated systems or where human review requires seeing content in its original spatial context.
The project shows consistent development activity with regular commits and ongoing refinement. The codebase demonstrates active maintenance through continuous updates to the model and supporting infrastructure. Documentation and examples receive regular attention, indicating the maintainers are responsive to practical usage patterns.
