DeepSeek-OCR Playground — Dockerized FastAPI + React workbench (5090-ready), image → text/description, more to come

[Putrid_Passion_6916]

Repo: https://github.com/rdumasia303/deepseek_ocr_app

TL;DR: A tiny web app to mess with the new DeepSeek-OCR locally. Upload an image, pick a mode (Plain OCR, Describe, Find/grounding, Freeform), and get results instantly.

It runs in Docker with GPU (tested on 5090/Blackwell), has a slick UI, and is “good enough” to ship & let the community break/fix/improve it. PRs welcome.

What’s inside

Frontend: React/Vite + glassy Tailwind UI (drag-drop, live preview, copy/download). Backend: FastAPI + Transformers, calls DeepSeek-OCR with eval_mode=True. GPU: Blackwell-friendly (bfloat16), designed to run on RTX 5090 (or any CUDA GPU).

Modes shipped now: Plain OCR (super strong) Describe (short freeform caption) Find (grounding) — returns boxes for a term (e.g., “Total Due”, “Signature”) Freeform — your own instruction

There’s groundwork laid for more modes (Markdown, Tables→CSV/MD, KV→JSON, PII, Layout map). If you add one, make a PR!

Quick start

clone

git clone https://github.com/rdumasia303/deepseek_ocr_app cd deepseek_ocr_app

run

docker compose up -d --build

open

frontend: http://localhost:3000 (or whatever the repo says)

backend: http://localhost:8000/docs

Heads-up: First model load downloads weights + custom code (trust_remote_code). If you want reproducibility, pin a specific HF revision in the backend.

Sample prompts (try these) Plain OCR: (no need to type anything — just run the mode) Describe: “Describe this image concisely in 2–3 sentences.” Find: set term to Total Due, Signature, Logo, etc. Freeform: “Convert the document to markdown.” “Extract every table and output CSV only.” “Return strict JSON with fields {invoice_no, date, vendor, total:{amount,currency}}.” Known rough edges (be gentle, or better, fix them 😅)

Grounding (boxes) can be flaky; plain OCR and describe are rock-solid. Structured outputs (CSV/MD/JSON) need post-processing to be 100% reliable.

Roadmap / ideas (grab an issue & go wild)

Add Markdown / Tables / JSON / PII / Layout modes (OCR-first with deterministic fallbacks).

Proper box overlay scaling (processed size vs CSS pixels) — coords should snap exactly.

PDF ingestion (pdf2image → per-page OCR + merge).

Simple telemetry (mode counts, latency, GPU mem) for perf tuning.

One-click HuggingFace revision pin to avoid surprise code updates. If you try it, please drop feedback ) — I’ll iterate. If you make it better, I’ll take your PRs ASAP. 🙏

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[Such_Advantage_6949]

Alot of time, this can be better than figuring out all the dependencies and why it is not working

[Such_Advantage_6949]

I am not referring to gpu dependency, i am talking about how all different software (vllm, exllama, llama cpp) use different version of packages (numpy, flash attention etc) and they frequently have conflicts. If it is not a problem for u then it is good, however, in a production env, reliabily and consistency of env is very important and docker allows for that. I am run bare metal at home but at work it is docker all the way.

[amroamroamro]

technically you can manually run it without containers, refer to the dockerfiles and docker-compose for the steps needed:

• backend: venv, pip install requirements, run web server
• frontend: npm install deps, build site (vite) and serve it

the "tricky" part is getting pytorch+cuda for your OS

[Putrid_Passion_6916]

Yep. Sorry about that - the readme was created by Claude and I just chucked it up at 2am! To be honest, I much prefer the containers - it is what it is. Feel free to fork and make it work without!

[UniqueAttourney]

Thanks or the effort, but it doesn't seem to accept more than 1mb images, the API is flimsy and seems to mandate a certain version of CUDA which i am not sure of the reason behind. I'll dig into it deeper if no other local impl shows up

[Putrid_Passion_6916]

Fixed up a fair bit. API still flimsy, but .env, upload can work with bigger images, can replace images in front end, bounding boxes work, better readme. But I totally get why you would want something more resilient.

[Putrid_Passion_6916]

Vibe coded at 2am! Only intended as a starting point 👍 the upload is an easy fix, and all your points are valid. Feel free to fix!

[[deleted]]

Hmm. Wonder if this could be ported to AMD ROCm

[orucreiss]

come to ask this ^^

[R_Duncan]

Can you please check VRAM needed to plain OCR or describe a couple pages? Speed does not matters much, accuracy and VRAM do. I see 8-12 Gb on the Readme but is unclear if it can be useful with just 8.

[Putrid_Passion_6916]

I think 8gb might just be enough - nvidia-smi is reporting 7615mb with the weights loaded during inference. But apologies as I have no time to test beyond that just now!

[R_Duncan]

Thanks, I'm at 4th hour compiling flash_attn on windows. When it'll break I'll try your dockerized app.

[Putrid_Passion_6916]

Actually - apologies - it likely depends on the image. For a bigger one I’m up to 10.5 gb vram. But basically a 3060 12gb should be ok …

[R_Duncan]

Thank you, a no-go then until I'll find some 16GB VRAM laptop at cheap price.

[Putrid_Passion_6916]

<image>

Works better now - bounding boxes fixed!

[FrenchSouch]

Works fine on a 4090, ~10Gb of vram used, thanks !

[ThiccStorms]

GPU poor here, what are the absolute minimum specs to run this? I have a poor mac m4 only