Florence-2 vs Surya

Florence-2, introduced by Microsoft Research at CVPR 2024, is an open-source vision-language foundation model designed to unify diverse computer vision tasks within a single sequence-to-sequence framework. Unlike traditional models that specialize in specific tasks, Florence-2 accepts both images and text prompts and outputs text for tasks such as captioning, object detection, segmentation, OCR, and region-based grounding. It comes in two sizes—Florence-2-base (~230M parameters) and Florence-2-large (~770M parameters)—and is trained on FLD-5B, a large dataset of ~126M images with ~5.4B annotations.

The model demonstrates strong zero-shot and fine-tuned performance, often rivaling larger vision-language systems while remaining lightweight and efficient. Released under the MIT license, all weights are publicly available, making it accessible for fine-tuning and deployment in applications like VQA, content tagging, accessibility, and research. Florence-2’s compact design, versatility, and openness position it as a practical alternative to larger proprietary multimodal models.

Surya is an OCR and document layout analysis toolkit developed by Vikram Paruchuri and distributed via Mindee, first released in January 2024 under the GPL-3.0 license. It supports text recognition across more than 90 languages, document layout detection, reading order prediction, table recognition, and equation detection, providing a comprehensive set of tools for extracting structured information from document images.

Surya is designed to operate without cloud API dependencies, running fully on local hardware with support for CPU and GPU inference. It is commonly used for digitizing scanned documents, extracting text from PDFs with complex layouts, and building automated document processing pipelines.