DeepReader-OCR

# DeepReader

[![DeepReader Logo](assets/deepreader_logo.png)](assets/deepreader_logo.png)

DeepReader is an agentic reading toolkit that couples DeepSeek-OCR with opinionated defaults for running single-document or batch OCR. It streamlines image/PDF ingestion, produces Markdown accompanied by figure crops and layout previews, and exposes knobs for both CLI and Gradio workflows.


## Project Layout

- `images/`: Sample page images for quick smoke-tests.
- `docs/`: Input PDFs for full-length papers.
- `outputs/`: Generated Markdown, annotated images, and layout PDFs.
- `DeepSeek-OCR-master/DeepSeek-OCR-vllm/`: vLLM-powered runtime (default entry points).

## Environment Setup

- download the vllm-0.8.5 [whl](https://github.com/vllm-project/vllm/releases/tag/v0.8.5) 

```bash
conda create -n deepreader python=3.12.9 -y
conda activate deepreader
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 \
  --index-url https://download.pytorch.org/whl/cu118
pip install vllm-0.8.5+cu118-cp38-abi3-manylinux1_x86_64.whl
pip install -r requirements.txt
```

Optional extras:

- `pip install flash-attn==2.7.3 --no-build-isolation` (faster attention if supported).

## Configuration Strategy

`DeepSeek-OCR-vllm/config.py` reads all defaults from environment variables, making it easy to swap inputs, outputs, prompts, or GPU settings without editing code.

```bash
export DEEPREADER_INPUT_PATH="$PWD/docs/paper.pdf"
export DEEPREADER_OUTPUT_PATH="$PWD/outputs/paper_run"
export DEEPREADER_PROMPT='<image>
<|grounding|>Convert the document to markdown.'
export DEEPREADER_PROMPT_TEMPLATE=document
export DEEPREADER_MODE=gundam
export DEEPREADER_CUDA_VISIBLE_DEVICES=0
export DEEPREADER_GPU_MEM_UTIL=0.8
export DEEPREADER_KEEP_MODELS_LOADED=1
```

>**GPU tip**: the default vLLM config assumes ≈10 GB of free VRAM. Tune `DEEPREADER_GPU_MEM_UTIL` down if you’re memory-constrained.

## Gradio Interface

[![DeepReader Gradio UI](assets/interface.png)](assets/interface.png)

Launch an inte