LightOnOCR-2-1B is a flagship OCR model refined with RLVR training for maximum accuracy, recommended for most OCR tasks. It is an efficient end-to-end 1B-parameter vision-language model for converting documents into clean, naturally ordered text. This model achieves state-of-the-art performance on OlmOCR-Bench while being smaller and faster than competing approaches.
The model is fully differentiable, allowing for fine-tuning and supporting various tasks such as domain adaptation and multilingual fine-tuning. It can handle tables, receipts, forms, multi-column layouts, and math notation, making it a versatile tool for document understanding. The model also predicts bounding boxes for embedded images, enhancing its functionality.
LightOnOCR-2-1B is part of a model family that includes variants for specific tasks, such as base models for fine-tuning and models with image bounding boxes. The model is available for use with transformers and can be deployed using vLLM. It has been trained on a large and high-quality corpus, resulting in improved performance and efficiency. The model's capabilities make it suitable for a wide range of applications.
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