The most efficient approach for a local installation is leveraging Docker containers.
Refer to the instructions below to proceed.
An automated background process downloads all required large-scale files.
The script runs a quick hardware check to dynamically adjust parameters for elite speed.
The DeepSeek-OCR-2 model sets a new benchmark in document understanding by combining high‑resolution image processing with a novel attention mechanism that captures contextual relationships across lines and paragraphs. Its architecture leverages a multi‑scale convolutional backbone, enabling robust performance on both printed and handwritten scripts while maintaining fast inference speeds on standard GPUs. A dedicated language‑agnostic tokenizer expands the model’s vocabulary to over 200 k subword units, supporting more than 100 languages and specialized domain terminologies. In comparative benchmarks, DeepSeek-OCR-2 achieves an average accuracy of 98.7 % on the DocVQA dataset, surpassing the previous state‑of‑the‑art by a margin of 1.4 %. The accompanying open‑source toolkit provides pre‑trained checkpoints, data augmentation pipelines, and a simple API, allowing developers to fine‑tune the model for custom OCR pipelines with minimal overhead.
| Model name | DeepSeek-OCR-2 |
| Parameters | 1.2B |
| Input resolution | 1024×1024 |
| Supported languages | 100 |
| Accuracy (DocVQA) | 98.7% |
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