Surya OCR

Surya OCR is a modern AI model developed for document-level multilingual optical character recognition, supporting over 90 languages with impressive accuracy across diverse document types. This high-performance model uses an encoder-decoder architecture based on the Donut framework, extracting image features with a Swin Transformer encoder and generating text with an mBART decoder. Unlike traditional OCR systems, Surya employs an end-to-end deep learning architecture that excels in complex document layouts where conventional rule-based approaches struggle significantly.

Surya's architecture includes a transformer-based text recognition module and an advanced layout analysis module that work in concert to understand document structure. The layout analysis automatically detects and classifies different elements in the document such as text blocks, tables, headers, footnotes, captions, and images with high precision. This provides users with rich structural information about the document, ensuring that text output faithfully reflects the original document format and reading order. Multi-column newspaper pages, complex nested table structures, interleaved lists, and mixed-layout academic papers are processed successfully with high fidelity. The line detection module can correctly identify and process skewed and rotated text segments.

The model can recognize multiple writing systems including Latin, Cyrillic, Arabic, Chinese, Japanese, Korean, and Indic scripts with consistent accuracy. This extensive language and script support provides significant advantages in international document processing, archive digitization, and multilingual content management projects across organizations. Turkish character recognition performance achieves high accuracy rates including all special characters (ç, ğ, ı, ö, ş, ü), making it reliable for Turkish-language document processing workflows and historical Ottoman document digitization efforts.

Surya OCR achieves competitive results on ICDAR benchmarks and demonstrates performance comparable to commercial solutions such as Google Cloud Vision and AWS Textract, while remaining completely free and open source without usage limits. Compared to traditional OCR tools like Tesseract, its performance is notably superior especially in handwriting recognition, low-resolution scans, degraded documents, and complex layouts. It works on PDFs, image files (JPEG, PNG, TIFF, WebP), and scanned documents with consistent quality regardless of input format or scanning conditions.

Available as open source on GitHub, Surya OCR can be easily installed via pip and used programmatically through its comprehensive Python API. The CLI tool supports batch document processing and offers a scalable batch processing pipeline for automatic digitization of large archives and document collections. It produces structured output in JSON and hOCR formats, facilitating integration with search engines, document management systems, and downstream applications for indexing and retrieval. It performs fast inference on GPU while also delivering reasonable performance on CPU for smaller workloads.

Serving as an ideal solution for document digitization, archive scanning, invoice processing, contract analysis, medical record transcription, legal document processing, and accessibility applications, Surya OCR provides a powerful and free alternative for researchers, developers, and organizations with document processing automation needs at any scale. Its active developer community and regular updates ensure continuous improvement in recognition accuracy, expanded language and script coverage, and enhanced processing speed for production deployments.