How to Install Bytedance Dolphin – A Document Image Parser

Introduction

The Bytedance Dolphin document image parser is revolutionizing how we understand and extract information from complex documents. The demand for accurate layout understanding and parsing from image-based documents continues to grow. The Bytedance Dolphin document image Parser addresses this need with an OCR-free, prompt-based approach to extracting structured data from scanned documents, invoices, academic reports, and complex PDFs, many of which remain locked in unstructured image formats. Traditional OCR tools like Tesseract or layout-aware models such as LayoutLM struggle with complex layouts, multilingual content, and dynamic document structures.

Recent breakthroughs such as SmolDocLing, DocLing, and Microsoft MarkItDown have pushed the boundaries of document image parsing and layout understanding, but they still depend on OCR pipelines or predefined schemas. This is where Dolphin, an innovative open-source document image parsing framework from ByteDance, introduces a transformative shift. Unlike traditional methods, Dolphin is completely OCR-free. It frames document parsing as a prompt-driven multimodal generation task, combining the power of visual encoders with large language models (LLMs) to intelligently understand and extract information, without retraining or rigid schemas.

With just a prompt like “Extract all tables in Markdown” or “Convert this invoice to JSON”, Dolphin delivers structured, human-readable results — all without retraining or manual setup. As an open-source, locally deployable solution, it’s ideal for enterprises prioritizing privacy, scalability, and flexible document intelligence.

With Dolphin, users can simply describe what they want in natural language prompts, enabling zero-shot extraction of structured data from a wide variety of document types. Whether you’re parsing tables, key-value pairs, paragraphs, or multi-column layouts, Dolphin provides flexible, high-quality outputs that are human-readable and structured.

In this guide, we’ll explore why the Bytedance Dolphin document parser stands out in 2025, how it compares to other models, and how you can install and use it locally for your document parsing needs.

Why Bytedance Dolphin Document Image Parser Outperforms OCR Tools?

Dolphin is a document image parser’s framework designed around a simple but powerful idea: instead of rigid rules or pipelines, let users describe what they want in natural language. By combining visual perception with language understanding, Dolphin enables zero-shot extraction of structured information from complex document images.

This allows users to interact with documents the way they might instruct a human:

• “Extract the tables in Markdown format.”
• “Summarize the contents of this report in bullet points.”
• “Convert this invoice into structured JSON.”

Dolphin turns these prompts into structured outputs—accurate, context-aware, and flexible across different layouts, languages, and domains.

Bytedance Dolphin Document Image Parser Architecture Explained

The architecture behind Dolphin is what empowers its cross-modal understanding and generation abilities. It consists of two main components:

• A Vision Encoder: This backbone processes the document image and converts it into a rich visual representation, capturing not only the text but also layout, visual cues, and embedded structures like tables or diagrams.
• A Language Decoder (LLM): This component takes both the visual representation and the user’s prompt as input and generates structured text as output. The decoder uses cross-attention mechanisms to align language understanding with visual layout reasoning.

The prompt acts as a task specifier, and the model generates responses tailored to it. Because Dolphin is trained in a task-agnostic manner, this architecture supports a wide variety of use cases out of the box.

Dolphin’s Prompt-Based Approach in Action

One of Dolphin’s core innovations is treating document parsing as a natural language generation problem. This makes it highly adaptable. With the right prompt, users can instruct Dolphin to return content in almost any structured format.

For example:

• A financial analyst can input a scanned invoice and prompt Dolphin to return the item list with prices in JSON.
• A legal researcher could upload a contract and prompt the model to highlight and summarize clauses.
• A scientist might prompt the model to extract all tables from a research paper in Markdown.

This interaction model makes Dolphin extremely versatile and eliminates the need to hard-code different workflows for each use case.

Output Formats: JSON, Markdown, Text

Another strength of the Dolphin document image parser is its support for structured output formats. Instead of just returning plain extracted text, it can format the results based on the prompt. Currently supported formats include:

• JSON: Ideal for key-value pairs, form data, invoices, and receipts.
• Markdown: Perfect for extracting tables, lists, or documents meant for human consumption.
• Plain Text: For general summaries, bullet points, or raw content extraction.

The choice of output format gives developers full control over how the data is structured for downstream applications, be it in databases, APIs, or document viewers.

Comparison with Existing Methods

The field of document parsing has seen significant evolution in recent years, with approaches ranging from classical OCR pipelines to transformer-based vision-language models. While models like LayoutLMv3, PromptDoc, DocFormer, and the newer Docling and SmolDocling have all contributed to progress, Dolphin introduces a unique prompt-based paradigm that unlocks unmatched flexibility and performance.

Unlike earlier models that often require fine-tuning or domain-specific training, Dolphin is entirely task-agnostic and operates in a zero-shot setting. It treats document parsing as a generative task, allowing users to specify their extraction goals using natural language, without modifying the architecture or retraining for each new use case.

Let’s explore how Dolphin compares to the most relevant competitors:

LayoutLMv3 and DocFormer

These models focus on understanding document structure through token-level classification and bounding box attention. While they excel in tasks like key-value extraction or layout-aware classification, they suffer from several limitations:

• Rigid output: The output format is task-specific and requires architectural changes to support new formats (e.g., JSON, Markdown).
• Fine-tuning required: New tasks typically demand retraining on annotated datasets.
• Limited generalization: Performance drops when layout or domain shifts occur.

In contrast, Dolphin’s generative model handles all these challenges via prompt engineering, requiring no fine-tuning and generalizing effortlessly to unseen layouts.

Feature / Model	Dolphin (Prompt-based)	LayoutLMv3	DocFormer	PromptDoc	Docling	SmolDocling
Input Type	Image + Natural Prompt	Tokenized image-text	OCR + Layout	Prompt-based	Image + Layout + Text	Image + Layout + Text
Output Format	JSON / Markdown	Tokens only	Pre-defined	Free-form	Structured Text	Structured Text
Training Requirement	❌ None (Zero-shot)	✅ Yes	✅ Yes	⚠️ Some	✅ Yes	✅ Yes
Use Case Flexibility	✅ Very High	❌ Low	⚠️ Medium	✅ High	⚠️ Medium	⚠️ Medium
Performance (SOTA)	✅ Best in Class	⚠️ Medium	⚠️ Medium	✅ High	⚠️ Good (Efficient)	⚠️ Good (Fast)
Lightweight Deployment	⚠️ Medium (Large Model)	❌ No	❌ No	⚠️ No	✅ Yes	✅ Yes
Custom Format Support	✅ Full (Prompt-defined)	❌ Limited	❌ Limited	⚠️ Moderate	❌ Predefined	❌ Predefined
Architecture Adaptability	✅ One Model Fits All	❌ Task-specific	❌ Task-specific	⚠️ Somewhat Adaptable	❌ Task-specific	❌ Task-specific

PromptDoc

PromptDoc also embraces the idea of prompt-based document understanding but is built around LayoutLMv3 and still relies on classification heads. While more flexible than traditional models, it is constrained to predefined formats and limited parsing logic. Dolphin surpasses it by treating the entire process as sequence generation, resulting in higher fidelity and more structured output.

SmolDocling and Docling

Both SmolDocling and Docling are recent lightweight models designed for document understanding with layout awareness. These models use smaller backbones and efficient multimodal embeddings to extract information while keeping computational overhead low.

• Docling offers efficient document parsing by fusing text and layout information, performing well on structured extraction tasks with minimal resources.
• SmolDocling focuses on high-speed inference and lower memory use, targeting resource-constrained environments such as mobile or edge devices.

However, both models are still limited by the need for structured schema definitions and fine-tuned architectures for each use case. Dolphin, on the other hand, is capable of handling unseen tasks in a zero-shot fashion, supports natural language prompts, and outputs highly structured formats such as JSON or Markdown—all without changing the underlying model.

Why Bytedance Dolphin document image parser Wins

Dolphin document image parser’s superiority lies in its ability to combine visual and linguistic understanding with prompt-driven flexibility. Where other models rely on classification, bounding box alignment, or pre-defined schemas, Dolphin listens to your instruction and generates exactly what you need.

In short, Dolphin is not just a model—it’s an interface between humans and unstructured documents, closing the gap between OCR and true document intelligence.

Benchmark Results: How Well Does Dolphin Perform?

Dolphin, as an OCR-free document parser, delivers state-of-the-art performance across multiple document understanding benchmarks, including Table-PubTabNet, Table-PubTab1M (TEDS), Formula datasets (SPE, SCE, CPE), and layout-heavy datasets like FoxBlock and FoxPage.

The radar chart below, adapted from the official Dolphin research paper, compares Dolphin with top-tier models including Qwen2-VL-7B, GOT, GPT-4o, and Claude 3.5 Sonnet. Dolphin consistently performs at or near the top across all tasks, including both semantic precision and structural fidelity.

This result validates Dolphin as a new state-of-the-art model for prompt-based document image and layout parsing, suitable for real-world applications with zero-shot needs. Dolphin has also been benchmarked across several datasets and demonstrates state-of-the-art performance:

• PubLayNet: Outperforms LayoutLMv3 with +2.5% in overall F1 score.
• CORD (Receipts): Achieves an exact match score of 91.3%, surpassing fine-tuned competitors.
• DocVQA: Shows leading performance on visual question answering benchmarks.

These results highlight Dolphin’s ability to generalize across tasks and domains, even when evaluated in zero-shot scenarios.

Step-by-Step Local Installation Guide

Setting up Dolphin locally is straightforward for developers with a Python and GPU environment.

1. Clone the GitHub repository:

git clone https://github.com/bytedance/Dolphin.git
cd Dolphin

2. Install dependencies:

pip install -r requirements.txt

3. Login to Hugging Face to access model weights:

mkdir hf_model
cd hf_model

4. Download the Model by Hugging Face CLI

Visit our Huggingface model card, or download the model by:

huggingface-cli download ByteDance/Dolphin --local-dir ./hf_model

5. Running Dolphin on Your Images

Once you’ve set up the model locally using Hugging Face and downloaded the required weights to ./hf_model, you can run Dolphin on document images using the demo_page_hf.py script.

• Process a Single Document Image

python demo_page_hf.py --model_path ./hf_model --input_path ./demo/page_imgs/page_1.jpeg --save_dir ./results

• --model_path: Path to the locally stored Hugging Face model directory.
• --input_path: Path to a single document image file.
• --save_dir: Output directory where Dolphin will save the parsed results (in Markdown/JSON format).

• Process All Images in a Directory

python demo_page_hf.py --model_path ./hf_model --input_path ./demo/page_imgs --save_dir ./result

If you have multiple images in a directory, Dolphin will parse each file automatically. This is especially useful for processing scanned PDFs converted to images.

• Speed Up with Custom Batch Size

python demo_page_hf.py --model_path ./hf_model --input_path ./demo/page_imgs --save_dir ./results --max_batch_size 16

Use --max_batch_size to enable parallel decoding of elements (like tables, formulas, blocks), which significantly speeds up large-scale parsing.

• Output Format

The results are saved in your --save_dir directory in:

• JSON format: Ideal for programmatic use and downstream NLP tasks
• Markdown format (.md): Easy to read or convert to DOCX/PDF.

Official GitHub: https://github.com/bytedance/Dolphin/tree/master

Conclusion

Bytedance Dolphin Document Image Parser marks a turning point in intelligent document understanding. Its unique blend of vision and language models, prompt-based tasking, and structured output generation offers a flexible, powerful, and open-source alternative to traditional OCR or layout models. Whether you’re parsing thousands of invoices, building a document chatbot, or developing an AI-powered filing system, Dolphin is a future-proof solution worth exploring.

With Dolphin, we’re no longer limited by templates or retraining cycles. We can simply prompt, parse, and proceed.

Frequently Asked Questions (FAQs)

1. What is Dolphin in document parsing?
Dolphin is an OCR-free, prompt-based AI model by ByteDance that extracts structured data from scanned document images using vision + LLMs.

2. Can Dolphin extract tables, formulas, images, and layout boxes?
Yes. Dolphin can extract tables, math formulas, images, charts, and layout boxes from document images using natural language prompts.

3. Is Dolphin better than OCR tools like Tesseract?
Often, yes. Dolphin handles complex layouts, tables, and multilingual text better—without relying on OCR pipelines or retraining.

4. Can Dolphin run locally?
Yes. Dolphin supports local installation and inference, making it a privacy-friendly tool for enterprises and developers.

5. How is Dolphin different from SmolDocLing and DocLing?
Unlike SmolDocLing and DocLing, Dolphin is OCR-free and prompt-driven, enabling zero-shot extraction directly from raw images.

6. Can Dolphin parse PDFs and scanned images?
Yes. Convert PDFs to images (JPEG, PNG), and Dolphin can parse them with high accuracy.

7. What are Dolphin’s hardware requirements?
It runs on GPUs like RTX 3060 or Tesla T4. For larger models, ≥12 GB VRAM is recommended.

8. Can Dolphin handle multilingual documents?
Yes. Dolphin supports multilingual parsing using language-agnostic LLMs, no need for OCR-based tokenizers.

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