Hunyuan OCR: The End-to-End, Multilingual OCR Engine Creators Can Actually Deploy

Why creators should care about Hunyuan OCR#

If your creative workflow touches text in images, PDFs, design assets, or video frames, Hunyuan OCR is the rare upgrade that saves time across the board. Built by Tencent Hunyuan as a 1B-parameter, end-to-end Vision-Language Model, Hunyuan OCR folds the entire OCR stack—detection, recognition, parsing, extraction, even translation—into one model. That means fewer moving parts, fewer brittle glue scripts, and fewer downstream errors that derail your pipeline.

For content creators—video editors pulling subtitles, designers localizing layouts, writers researching documents, or voice actors batch-processing scripts—Hunyuan OCR combines strong accuracy with practical speed and deployment simplicity. It supports 100+ languages, runs efficiently with vLLM or Transformers, and pairs clean, task-oriented prompts with production-friendly inference routes.

In this guide, you’ll learn what sets Hunyuan OCR apart, what it can do for your specific creative role, and how to get it running in minutes.

What makes Hunyuan OCR different#

Traditional OCR pipelines daisy-chain multiple models and heuristics: detect text regions, crop, recognize characters, post-process, and then parse structure. Each hop can introduce errors that compound. Hunyuan OCR’s end-to-end approach simplifies this stack so you can go from image to structured output in a single forward pass.

Key differentiators:

End-to-end design: Hunyuan OCR avoids the error propagation common in cascaded OCR stacks by keeping detection, recognition, and downstream understanding under one roof.
Lightweight power: Hunyuan OCR achieves state-of-the-art results with only 1B parameters, making it practical to ship and scale.
Multilingual reach: Hunyuan OCR supports 100+ languages, unlocking global content production and localization.
Broad task coverage: Hunyuan OCR handles text spotting, document parsing, information extraction, video subtitle extraction, image translation, and document question answering.
Plug-and-play deployment: Hunyuan OCR can run with vLLM for high-throughput serving or with Transformers for flexible scripting workflows.

According to published benchmarks in the official repository and technical report, Hunyuan OCR delivers SOTA performance on document parsing (e.g., OmniDocBench) and strong results in text spotting and information extraction on in-house evaluations, while competing closely on image translation—all with a compact model size.

What Hunyuan OCR can do for creators#

Hunyuan OCR is designed to solve practical creator problems with minimal friction:

Video subtitle extraction
- Pull subtitles from frames or clips.
- Convert burned-in captions into time-aligned text for editing.
- Build multilingual subtitle drafts for translation.
Document parsing and layout understanding
- Convert PDFs, forms, and brochures into structured fields.
- Extract tables, headers, lists, and reading order.
- Generate JSON-ready outputs for CMS ingestion.
Information extraction for receipts, invoices, and IDs
- Extract vendor names, totals, date fields, addresses, and IDs.
- Enforce a fixed schema for batch processing.
Image translation for creative assets
- Translate text in posters, social graphics, UI screens, or comics.
- Retain layout semantics to guide re-typesetting.
Document QA for research-heavy workflows
- Ask questions of long documents and receive targeted answers with evidence.
- Cross-check fields extracted from complex filings.

For each of these tasks, Hunyuan OCR centers on “application-oriented prompts,” so you can steer outputs toward structured formats that slot into your existing tools.

Performance at a glance#

While your results will vary by domain, the authors report:

Text spotting: Hunyuan OCR outperforms several popular OCR and VLM baselines on an in-house benchmark.
Document parsing: Hunyuan OCR reaches SOTA on OmniDocBench and a multilingual internal suite, surpassing large general VLMs and specialized OCR-VLMs.
Information extraction: Hunyuan OCR shows strong gains on cards, receipts, and subtitle extraction tasks in internal evaluations.
Image translation: Hunyuan OCR offers accuracy comparable to far larger models while remaining deployable.

These results, paired with its 1B-parameter footprint, make Hunyuan OCR a compelling upgrade if you’ve struggled to deploy bulkier OCR/VLM stacks.

References:

Demo: https://huggingface.co/spaces/tencent/HunyuanOCR
Model: https://huggingface.co/tencent/HunyuanOCR
GitHub repository and technical report (see HunyuanOCR_Technical_Report.pdf and https://arxiv.org/abs/2511.19575)

Inside the model: how Hunyuan OCR works#

Under the hood, Hunyuan OCR connects a native Vision Transformer (ViT) encoder to a lightweight LLM via an MLP adapter. This allows the vision side to capture dense text patterns—fonts, scripts, layouts—while the language side reasons over structure, schemas, and instructions. The result is unified OCR-plus-understanding behavior driven by prompts.

The technical report also describes reinforcement learning strategies that further improve OCR-specific instruction following and output quality. Practically, that means Hunyuan OCR can be steered with highly specific prompts (e.g., “extract only totals as USD and return ISO dates”), which is vital for creators who need clean, ready-to-use outputs.

System requirements and installation#

Hunyuan OCR publishes code, weights, and quick-starts for both vLLM and Transformers. For production throughput, vLLM is recommended; for custom scripts or prototyping, Transformers works well.

Minimum environment (per repository guidance):

OS: Linux
Python: 3.12+
CUDA: 12.9
PyTorch: 2.7.1
GPU: NVIDIA GPU with CUDA support (around 20 GB memory recommended for vLLM serving)
Disk: ~6 GB for weights

Installation paths:

With vLLM (serving): install vllm, download the model from Hugging Face, and start an API server.
With Transformers (scripting): install transformers and accelerate, then load the checkpoint and run inference.

Hunyuan OCR exposes clear scripts for both routes in the repo’s README.

Quick-start: Hunyuan OCR with vLLM#

Install vLLM and dependencies:

pip install vllm

Launch a vLLM server with Hunyuan OCR:

python -m vllm.entrypoints.openai.api_server \
  --model tencent/HunyuanOCR \
  --trust-remote-code \
  --tensor-parallel-size 1 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.9 \
  --port 8000

Call the server via OpenAI-compatible API:

import base64, requests

def encode_image(path):
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

image_b64 = encode_image("invoice.jpg")
prompt = """You are an OCR and information extraction assistant.
Task: Extract vendor_name, date(YYYY-MM-DD), total_amount(USD), and line_items from the image.
Return valid JSON with these keys only and no extra text."""

payload = {
  "model": "tencent/HunyuanOCR",
  "messages": [
    {"role": "user", "content": [
      {"type": "text", "text": prompt},
      {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}}
    ]}
  ],
  "temperature": 0.0
}
r = requests.post("http://localhost:8000/v1/chat/completions", json=payload, timeout=120)
print(r.json()["choices"][0]["message"]["content"])

In this setup, Hunyuan OCR responds with structured JSON you can feed straight into your pipeline.

Quick-start: Hunyuan OCR with Transformers#

Install dependencies:

pip install "transformers>=4.45.0" accelerate torch torchvision

Run a simple inference:

from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import json

model_id = "tencent/HunyuanOCR"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForVision2Seq.from_pretrained(model_id, trust_remote_code=True).eval().cuda()

image = Image.open("receipt.png").convert("RGB")
prompt = (
  "Detect all text regions and recognize their content. "
  "Return a JSON array of {bbox:[x1,y1,x2,y2], text:'...'}."
)

inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=1024)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)

Transformers lets you iterate quickly on prompts, integrate with notebooks, and compose Hunyuan OCR with other Python tools.

Prompt design: make Hunyuan OCR work for you#

Because Hunyuan OCR is end-to-end and instruction-following, your prompt is your interface. Clear, constrained prompts yield clean outputs.

General tips:

State the task, schema, and output format explicitly.
For structured data, ask for strict JSON and list the keys in order.
For multilingual inputs, specify source and target languages.
For layout tasks, request bounding boxes or reading order as needed.
Keep temperature low (0–0.2) for deterministic outputs.

Prompt templates you can adapt:

Text spotting
- “Detect all text regions and recognize their content. Return a JSON array of objects {bbox:[x1,y1,x2,y2], text:'...'} in reading order.”
Document parsing
- “Parse this document into title, subtitle, sections, tables, and footnotes. For each table, include a 2D array of cells. Return a JSON with fields: title, subtitle, sections[], tables[], footnotes[].”
Information extraction for receipts
- “Extract vendor_name, date (YYYY-MM-DD), currency (ISO code), subtotal, tax, total, and line_items[{name, qty, unit_price, amount}]. Return valid JSON with these exact keys. If a value is missing, set it to null.”
Subtitle extraction from video frames
- “Identify subtitle text on the image. Return an array of {bbox, text} for each subtitle line. If the text spans multiple lines, keep each line separate.”
Image translation
- “Translate all visible text from [SOURCE_LANGUAGE] to [TARGET_LANGUAGE]. Keep the layout order and return an array of {bbox, source, target}. Do not add explanations.”

Prompting is where Hunyuan OCR shines: you can get from unstructured pixels to structured JSON or bilingual outputs without round-trips between separate OCR and NLP modules.

Workflow recipes for creators#

Below are practical ways creators can fold Hunyuan OCR into daily work.

Video creators
- Batch subtitle recovery: Sample one frame per second, run Hunyuan OCR with a subtitle-spotting prompt, and assemble a rough SRT with timestamps. Clean-up becomes drastically faster.
- Foreign-language captions: Run Hunyuan OCR to extract text, then translate via an image translation prompt to create draft bilingual subtitles.
Designers and localization teams
- Poster and UI translation: For each asset, use Hunyuan OCR to extract text with bounding boxes, translate, and hand off {bbox, target} to designers for re-typesetting in Figma or Photoshop.
- Layout QA: Ask Hunyuan OCR for reading order and section headers to verify that responsive layouts still read logically.
Writers, researchers, editors
- Document scanning to notes: Use Hunyuan OCR to parse PDFs into sections and quotes for immediate editorial use.
- Fact extraction: Prompt Hunyuan OCR to extract key fields (dates, figures, entities) across scanned archives and return a unified dataset.
Voice actors and dubbing studios
- Line isolation: If scripts are embedded in storyboards or manga panels, have Hunyuan OCR extract line-by-line text, preserving panel order.
- Pronunciation context: Use Hunyuan OCR to capture original-language names and terms alongside translations for accurate delivery.

Each of these benefits from Hunyuan OCR’s end-to-end behavior, lowering the odds of pipeline breakage and massively reducing glue code.

Deployment: vLLM vs. Transformers#

vLLM for serving
- When you need a server to handle multiple users, batches, or high throughput, vLLM is the fastest way to host Hunyuan OCR.
- Tips:
  - Start with a 20 GB+ GPU for smooth throughput.
  - Use low temperature and set max tokens appropriate for your output size.
  - Warm up the server with a few sample requests to stabilize latency.
Transformers for scripting
- When you’re prototyping prompts, running offline batches, or building small bespoke tools, Transformers offers flexibility.
- Tips:
  - Preprocess images for consistent DPI and orientation.
  - Cap output tokens to keep runs predictable.
  - Cache the model and processor on disk for faster startups.

Whichever route you choose, you can keep the same prompts and swap backends when you move from prototype to production—another win for Hunyuan OCR.

Practical considerations and best practices#

Image quality matters
- Even with robust recognition, Hunyuan OCR benefits from sharp images. De-skew, denoise, and upscale where feasible.
Be explicit with schemas
- For extraction tasks, enforce field names and types. Hunyuan OCR responds well to precise instructions and JSON exemplars.
Batch intelligently
- In vLLM serving, batch multiple requests or frames when possible to boost throughput with Hunyuan OCR.
Monitor outputs
- Add validators for date formats, currency codes, or numeric ranges. If a value fails validation, re-prompt Hunyuan OCR with a corrective instruction.
Respect privacy
- Sensitive IDs, medical receipts, or contracts should be handled under your org’s data policies. Self-hosting Hunyuan OCR gives you tighter control than third-party APIs.
Know your limits
- Very long multi-page documents may require chunking. Use page-by-page prompts and stitch results, or ask Hunyuan OCR to summarize sections progressively.

Architecture and training notes (for the curious)#

A lean architecture powers Hunyuan OCR:

Vision backbone: A native ViT handles dense text features and layout cues.
Language head: A compact LLM performs instruction following and structured generation.
MLP adapter: Bridges vision embeddings and the language head.
RL strategies: As reported, reinforcement learning contributes notable gains on OCR-style instructions, improving adherence to formats and schemas.

This mix explains why Hunyuan OCR can be steered precisely—asking it for strict JSON or bilingual aligned outputs works reliably compared to traditional OCR stacks.

Step-by-step: building a document parsing pipeline#

To see Hunyuan OCR in action, here’s a simple PDF-to-structured-JSON flow:

Convert pages to images (e.g., 300 DPI PNGs).
For each page, prompt Hunyuan OCR to parse sections, headers, tables, and footers.
Validate: ensure every table has the same column count per row; coerce dates to ISO.
Merge: combine page-level results; reflow sections in reading order.
Export: store the final JSON in your CMS or data warehouse and keep a hash of the source file.

A single model means fewer integration headaches and less maintenance—one of the biggest advantages of Hunyuan OCR for small and mid-sized teams.

Where to try, download, and learn more#

Live demo: Explore Hunyuan OCR in your browser on Hugging Face Spaces
- https://huggingface.co/spaces/tencent/HunyuanOCR
Model weights: Download Hunyuan OCR from Hugging Face
- https://huggingface.co/tencent/HunyuanOCR
Source code and setup: Full repository with instructions, prompts, and evaluation details
- GitHub (search for HunyuanOCR)
Technical report: Methods, ablations, and RL strategies
- https://arxiv.org/abs/2511.19575 (also included as HunyuanOCR_Technical_Report.pdf in the repo)

Conclusion: a practical OCR upgrade for modern creative teams#

Hunyuan OCR brings end-to-end OCR, multilingual coverage, and strong accuracy into a compact 1B-parameter package you can actually deploy. Instead of stitching together detection, recognition, parsing, and translation, you prompt one model to return exactly what your workflow needs—clean JSON, aligned translations, or time-stamped subtitles.

For content creators who live in documents, frames, and design files, Hunyuan OCR enables:

Faster turnaround with fewer tools
Cleaner, schema-consistent outputs
Reliable multilingual processing
Straightforward deployment via vLLM or Transformers

If you’ve been waiting for an OCR engine that fits into real production while keeping developer overhead small, Hunyuan OCR is the right place to start. Try the demo, load the model, and see how much time you can win back this week.