# Optical Character Recognition

DeepSeek-VL2 is a series of advanced large-scale mixture of experts (MoE) visual language models, significantly improved compared to its predecessor DeepSeek-VL. This model series demonstrates exceptional capabilities across various tasks, including visual question answering, optical character recognition, document/table/chart understanding, and visual localization. Comprising three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2, with 1 billion, 2.8 billion, and 4.5 billion active parameters respectively, DeepSeek-VL2 achieves competitive or state-of-the-art performance against existing dense and MoE-based open-source models, even with a similar or fewer number of active parameters.

DeepSeek-VL2 is a series of large Mixture-of-Experts visual language models, showing significant improvements over its predecessor, DeepSeek-VL. This series exhibits exceptional performance in tasks such as visual question answering, optical character recognition, document/table/chart understanding, and visual localization. DeepSeek-VL2 includes three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2, with 1.0B, 2.8B, and 4.5B active parameters, respectively. Compared to existing open-source dense and MoE base models with similar or fewer active parameters, DeepSeek-VL2 achieves competitive or state-of-the-art performance.

EAGLE is a series of high-resolution, vision-centric multimodal large language models (LLMs) designed to enhance the perception capabilities of multimodal LLMs through a combination of visual encoders and varied input resolutions. The model features a 'CLIP+X' fusion based on channel connections, suitable for visual experts trained on different architectures (ViT/ConvNets) and domains (detection/segmentation/OCR/SSL). The EAGLE model family supports input resolutions over 1K and excels in multimodal LLM benchmarks, particularly in resolution-sensitive tasks such as optical character recognition and document understanding.

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