Dolphin v2: A Practical Guide to Next‑Gen Document Image Parsing for Creative Workflows

Overview: Why Dolphin v2 Matters for Content Creators#

Dolphin v2 is an open-source document image parsing model designed to convert complex visual documents—like scanned PDFs, receipts, forms, slides, magazines, and storyboards—into structured, machine-readable outputs. For content creators who routinely wrestle with messy inputs and time-consuming admin tasks, Dolphin v2 promises a faster route from raw files to useful assets you can edit, search, and automate.

Whether you’re a video creator extracting scripts from PDFs, a designer parsing brand guidelines and style sheets, a writer compiling references from scanned books, or a voice actor organizing character line sheets, Dolphin v2 can turn unstructured document images into clean JSON, CSV, Markdown, or plain text. It’s open-source (MIT License), actively developed, and available on GitHub at https://github.com/bytedance/Dolphin, with models hosted via the community (see the project docs for Hugging Face links).

In this guide, we’ll outline what Dolphin v2 is, what’s new compared to v1, how it works, how to install and use it, common pitfalls, performance considerations, and practical creative use cases—so you can bring Dolphin v2 into your daily workflow with confidence.

What Is Dolphin v2?#

At a glance:

• Dolphin v2 is a document image parsing model that reads images or PDFs and outputs structured data.
• It targets OCR-free or OCR-light pipelines, minimizing dependency on brittle OCR steps.
• It supports diverse document types (forms, invoices, tables, charts, multi-column magazines, posters).
• It’s suitable for both quick local inference and scalable server deployments.
• It’s open-source under the MIT License, promoting commercial and research use.
• Code, models, demos, and docs are maintained via the official GitHub repository: https://github.com/bytedance/Dolphin.

Dolphin v2 is built to be practical, robust, and developer-friendly. It’s meant to reduce friction around document understanding and speed up complex pre-production or post-production tasks, where creators often spend hours manually transcribing, tagging, and reorganizing content.

What’s New in Dolphin v2 vs. v1#

Dolphin v2 focuses on quality-of-life improvements, robustness in real-world scenarios, and ease of integration. While exact implementation details evolve, creators can expect these key improvements:

• Robustness to real-world capture:

  • Better handling of skewed, low-light, or imperfect mobile scans.
  • Improved tolerance for noisy annotations, stamps, and watermarks.

• Better structure understanding:

  • More precise layout parsing for multi-column, multi-lingual publications.
  • Stronger handling of tables, charts, and key-value pairs common in forms and invoices.

• Longer document support:

  • Improved chunking, pagination awareness, and cross-page context.
  • Smoother stitching of structured outputs across multi-page PDFs.

• OCR-light/OCR-free modes:

  • Reduced need for a separate OCR step; when OCR is used, Dolphin v2 supports plug-in OCR engines as fallbacks.

• JSON-first outputs:

  • Cleaner, consistent schema for downstream automations in Notion, Airtable, Figma plugins, spreadsheets, or NLE scripts.

• Streamlined deployment:

  • More straightforward server/API examples and faster cold-start for production usage.
  • Easier export to formats like CSV, Markdown, and HTML.

• Better developer experience:

  • Clearer configs, sample notebooks, and reference pipelines.
  • MIT License makes adoption in commercial pipelines straightforward.

Together, these refinements make Dolphin v2 easier to trust, faster to adopt, and more effective for creator-centric workflows of all sizes.

How Dolphin v2 Works (High-Level)#

While specific modules and training recipes are documented in the repo, here’s a conceptual view of how Dolphin v2 processes documents:

1. Visual encoding:

   • The input page image (from a PDF or a camera capture) is normalized and fed into a vision backbone to produce rich visual embeddings that are layout-aware.

2. Language and structure decoding:

   • A text decoder (often a transformer) generates structured tokens representing document content and layout elements (headers, paragraphs, lists, tables, cells, key-value pairs).

3. Schema-guided generation:

   • Dolphin v2 is tuned to produce structured outputs—commonly JSON—following a predictable schema that you can map to your apps.
   • This includes table cell coordinates, reading order, section headers, and association between labels and values in forms.

4. Optional OCR integration:

   • For specific languages or low-contrast images, an OCR plug-in may improve text fidelity. Dolphin v2 is flexible: use OCR-free mode for speed and simplicity, or hybrid mode for accuracy on tough cases.

5. Post-processing:

   • Outputs are standardized into formats your production tools can consume. Think CSV for spreadsheets, Markdown for docs and wikis, or JSON for automations and APIs.

For creators, the crucial point is that Dolphin v2 aims to minimize manual clean-up. You get structured content ready to edit, align, or publish—without rebuilding your pipeline from scratch.

System Requirements and Compatibility#

Dolphin v2 is designed to run on modern consumer and workstation setups. Typical requirements:

• OS: Linux or Windows (macOS for CPU inference; GPU acceleration varies by hardware)
• Python: 3.8–3.11 (check the repo for exact versions)
• Dependencies: PyTorch (GPU builds require CUDA support), OpenCV, Pillow, and other standard ML libraries
• Hardware:
  • CPU-only inference is possible for small jobs.
  • For real-time or batch throughput, a single modern GPU (e.g., 12–24 GB VRAM) is recommended.
  • Multi-GPU setups can accelerate large-scale processing across long PDFs or big archives.

Compatibility:

• PDFs are usually split into images per page; Dolphin v2 processes these page images (PNG/JPG).
• Integrates well with Python-based automation, REST APIs, and creative toolchains via JSON/CSV.
• MIT License makes Dolphin v2 easy to plug into proprietary workflows.

Always consult https://github.com/bytedance/Dolphin for the most accurate, up-to-date requirements.

Installation and Quickstart#

Dolphin v2 supports local and server deployments. The exact steps may vary; the following mirrors the typical flow in the official repo.

Option A: From source

# 1) Clone the repository
git clone https://github.com/bytedance/Dolphin.git
cd Dolphin

# 2) (Recommended) Create a clean environment
# Using Conda/Mamba as an example:
conda create -n dolphinv2 python=3.10 -y
conda activate dolphinv2

# 3) Install dependencies (see repo for the exact requirements file)
pip install -r requirements.txt

# 4) (Optional) Install GPU-enabled PyTorch per your CUDA version:
# Visit https://pytorch.org/get-started/locally/ for the right command

# 5) Download model weights as documented in the repo or model card
# e.g., scripts/download_weights.sh (if provided) or manual download

# 6) Run a quick inference demo (example command - check repo for specifics)
python tools/infer.py \
  --image_path ./samples/invoice_01.jpg \
  --output ./outputs/invoice_01.json \
  --config ./configs/dolphin_v2.yaml \
  --weights ./weights/dolphin_v2.pth

Option B: Use the provided notebook or demo app

• The repository often includes a Jupyter notebook with end-to-end examples.
• Some community builds publish Dolphin v2 on Hugging Face. If a prebuilt pipeline is available, try it with your browser or a Colab notebook.

Illustrative Python snippet (pattern only—refer to the repo for exact APIs):

from pathlib import Path
from PIL import Image
import json

# Pseudocode: the actual API names may differ
# e.g., dolphin.load_model(), dolphin.preprocess(), dolphin.postprocess()

# 1) Load model
model = load_dolphin_v2(weights_path="weights/dolphin_v2.pth", device="cuda:0")

# 2) Preprocess an image
img = Image.open("samples/storyboard_page.jpg").convert("RGB")
batch = preprocess_for_dolphin_v2([img])

# 3) Inference
with torch.no_grad():
    raw_outputs = model(batch)

# 4) Post-process to structured JSON
result = postprocess_dolphin_v2(raw_outputs)[0]

# 5) Save and inspect
Path("outputs").mkdir(exist_ok=True)
with open("outputs/storyboard_page.json", "w", encoding="utf-8") as f:
    json.dump(result, f, ensure_ascii=False, indent=2)

print("Extracted keys:", list(result.keys()))

Tip: Dolphin v2 typically returns structured elements like paragraphs, titles, tables with cells, or key-value fields for forms. You can convert those to CSV, Markdown, or your CMS schema.

Using Dolphin v2 in a Production API#

Many teams wrap Dolphin v2 in a lightweight REST service and call it from creative tools, NLEs, or automation scripts. A minimal FastAPI example (structure only; adapt to the repo’s functions):

from fastapi import FastAPI, UploadFile, File
from PIL import Image
import io, json

app = FastAPI()
model = load_dolphin_v2(weights_path="weights/dolphin_v2.pth", device="cuda:0")

@app.post("/parse")
async def parse_document(file: UploadFile = File(...)):
    content = await file.read()
    image = Image.open(io.BytesIO(content)).convert("RGB")
    batch = preprocess_for_dolphin_v2([image])
    with torch.no_grad():
        raw = model(batch)
    result = postprocess_dolphin_v2(raw)[0]
    return result  # FastAPI will serialize dict->JSON

Deploy this behind Nginx or a serverless GPU endpoint, and connect it to your MAM/DAM system, Google Sheets, Notion, or your own pipeline.

Performance and Benchmarks#

Performance depends on your GPU, input resolution, and document complexity. In general:

• Dolphin v2 aims to deliver higher accuracy than v1 on multi-column pages, forms, invoices, and noisy scans.
• Latency per page can be near real time on a single modern GPU, with batch processing accelerating multi-page PDFs.
• For best results, align input resolution with the model’s recommended settings (see configs).

Comparisons:

• Against traditional OCR + rule-based parsing, Dolphin v2 reduces brittle heuristics and manual clean-up.
• Versus older document understanding stacks, Dolphin v2 emphasizes layout, structure fidelity, and consistent schemas.
• Community reports indicate competitive results versus state-of-the-art OCR-free approaches on common benchmarks (e.g., FUNSD, SROIE, DocVQA-style tasks). For exact numbers and charts, see the repository’s benchmark section and model card.

Reproducible benchmarking tips:

• Fix the input resolution and batch size.
• Use a held-out set of your real documents (not only public datasets).
• Measure both precision (text fidelity, structure accuracy) and cost (latency, GPU memory).
• Log post-processing time; it matters in production.

Real-World Use Cases for Creators#

Dolphin v2 shines in everyday creative workflows:

• Video creators and editors:

  • Extract scripts and shot lists from PDFs and scanned notebooks.
  • Convert storyboards to structured data, making it easier to plan edits and track continuity.
  • Auto-generate subtitle drafts from slide decks with speaker notes.

• Designers and art directors:

  • Parse brand guidelines into searchable Markdown and component specs.
  • Extract color palettes, typography rules, and grid specs from styled PDFs.

• Writers and researchers:

  • Convert scanned references into clean, structured notes with citations and quotes.
  • Parse multi-column academic PDFs into sections while preserving reading order.

• Voice actors and audio producers:

  • Turn character sheets, call sheets, and sides into standardized CSVs for quick lookup.
  • Extract pronunciation guides and annotations into structured dictionaries.

• Freelancers and studios:

  • Automate invoice and receipt parsing for accounting and tax prep.
  • Process NDAs and contracts into key-value summaries (counterparties, dates, amounts).

In all cases, Dolphin v2 reduces repetitive manual work and frees up more time for creative decisions.

Integration Patterns and Best Practices#

• JSON-first: Keep Dolphin v2 output as JSON through your pipeline. Convert to CSV/Markdown only at the final step.
• Human-in-the-loop: For critical docs, add a quick review UI where editors can approve or correct outputs.
• Templates and prompts: If the repo provides schema templates or prompts, standardize across your team so outputs are predictable.
• Post-processing rules: Add light rules to handle edge cases (e.g., merging split lines, fixing OCR fallback quirks).
• Version pinning: Pin Dolphin v2 weights and config versions in production to avoid unexpected changes during updates.
• Storage: Save both raw images and Dolphin v2 JSON outputs for traceability and rapid reprocessing.

Licensing, Governance, and Community#

• License: MIT License—permissive, suitable for commercial and open-source use. See LICENSE in https://github.com/bytedance/Dolphin.
• Transparency: Check the repo’s README, model card, and changelogs for current limitations and intended use.
• Contributions: The project welcomes issues and pull requests. Open tickets for bugs, feature requests, or doc improvements.
• Community: Discussions and Q&A typically happen via GitHub Issues; look for links to any official forum or Hugging Face community threads in the repo.

By adopting Dolphin v2 under MIT, teams can safely integrate it into proprietary creative pipelines and products.

Troubleshooting Dolphin v2#

Common issues and fixes:

• Out-of-memory (OOM) on GPU:

  • Reduce input resolution or batch size.
  • Use mixed precision (AMP) if supported.
  • Switch to CPU for smaller jobs or use a GPU with more VRAM.

• Mismatched dependencies:

  • Ensure PyTorch/CUDA versions match your driver and OS.
  • Recreate a clean virtual environment and reinstall requirements.

• Incorrect reading order:

  • Enable or tune layout-aware settings in Dolphin v2 configs.
  • Preprocess inputs: deskew, increase contrast, crop margins.

• Table parsing errors:

  • Increase page resolution for documents with dense tables.
  • Verify table-detection thresholds in post-processing.

• Multilingual text issues:

  • Try OCR-hybrid mode for specific languages.
  • Update language packs and ensure fonts are available for rendering.

• Inconsistent JSON schema across versions:

  • Pin your Dolphin v2 version in production.
  • Add a converter step to normalize fields between versions.

• Poor results on photos of screens or glossy paper:

  • Avoid reflections; shoot in diffuse light.
  • Use a scanning app to enhance contrast and flatten perspective.

If you’re stuck, search existing issues or open a new one at https://github.com/bytedance/Dolphin with a minimal reproducible example.

Security and Privacy Considerations#

• Process sensitive documents locally when possible.
• If deploying Dolphin v2 as a service, secure the API (auth, rate limits, TLS).
• Log only what you need; avoid storing raw documents when unnecessary.
• Document retention policies should comply with your clients’ contracts and regulations.

Roadmap Considerations#

While the exact roadmap evolves, expect ongoing improvements in:

• Multilingual robustness and long-document handling
• Speed/memory optimizations
• Better table/chart understanding and figure captioning
• Developer tooling: upgraded demos, UI annotators, and benchmarking harnesses

Watch the repo for releases, tags, and changelog entries related to Dolphin v2.

Call to Action#

• Explore the code and docs: https://github.com/bytedance/Dolphin
• Try a sample: run Dolphin v2 on a few pages from your own workflow and measure the time savings.
• Share feedback: open issues, propose features, and contribute examples that help fellow creators.
• Integrate: wrap Dolphin v2 in a small API and plug it into your content pipeline this week.

Dolphin v2 aims to make document understanding feel like a native building block for creative teams. Start small, iterate fast, and let structured outputs do the heavy lifting while you focus on the craft.

FAQ#

Is Dolphin v2 officially released and open-source?#

Yes. Dolphin v2 is available in the official repository at https://github.com/bytedance/Dolphin and is open-source under the MIT License. Check the repo’s releases and tags for the latest version.

What’s the main difference between Dolphin v1 and Dolphin v2?#

Dolphin v2 improves real-world robustness, structured output consistency, table/form understanding, and ease of deployment. It also emphasizes smoother multi-page handling and JSON-first pipelines suitable for creative automation.

Can I use Dolphin v2 without a GPU?#

Yes, for small workloads. CPU inference is possible but slower. For production throughput or large PDFs, a modern GPU is recommended. Dolphin v2 benefits significantly from GPU acceleration.

Does Dolphin v2 require OCR?#

Not strictly. Dolphin v2 supports OCR-free modes and can integrate OCR as a fallback. For tough cases (low contrast, rare scripts), a hybrid setup may improve accuracy.

How do I install Dolphin v2?#

Clone the repo, create a clean Python environment, install requirements, download model weights, and run the sample inference script. Exact steps and commands are documented in the Dolphin v2 repository.

What file formats can Dolphin v2 output?#

Dolphin v2 typically outputs structured JSON, which can be converted to CSV, Markdown, or HTML. Many teams keep JSON during processing and only convert at the end.

Is Dolphin v2 suitable for commercial use?#

Yes. Dolphin v2 is released under the MIT License, which is permissive and friendly to commercial adoption. Review the LICENSE file in the repo for details.

How does Dolphin v2 compare to alternatives?#

Dolphin v2 aims to be robust and practical for real-world, creative workflows. Compared with OCR-plus-rules stacks, it reduces brittle heuristics. Against modern document parsers, Dolphin v2 is competitive and often easier to integrate. Evaluate on your own documents for a fair comparison.

Where can I get support for Dolphin v2?#

Use GitHub Issues in the official repository for bug reports, questions, and feature requests. The repo may also link to a Hugging Face model card or community threads.

What are best practices for deploying Dolphin v2 in production?#

Pin versions, run a review step for critical docs, log performance metrics, and secure your API. Start with a small service that returns JSON and scale as your throughput needs grow.