tiiuae/Falcon-OCR

Falcon OCR

Falcon OCR is a 300M parameter early-fusion vision-language model for document OCR. Given an image, it can produce plain text, LaTeX for formulas, or HTML for tables, depending on the requested output format.

Most OCR VLM systems are built as a pipeline with a vision encoder feeding a separate text decoder, plus additional task-specific glue. Falcon OCR takes a different approach: a single Transformer processes image patches and text tokens in a shared parameter space from the first layer, using a hybrid attention mask where image tokens attend bidirectionally and text tokens decode causally conditioned on the image.

We built it this way for two practical reasons. First, it keeps the interface simple: one backbone, one decoding path, and task switching through prompts rather than a growing set of modules. Second, a 0.3B model has a lower latency and cost footprint than 0.9B-class OCR VLMs, and in our vLLM-based serving setup this translates into higher throughput, often 2–3× faster depending on sequence lengths and batch configuration. To our knowledge, this is one of the first attempts to apply this early-fusion single-stack recipe directly to competitive document OCR at this scale.

Links

• Code and inference engine: https://github.com/tiiuae/Falcon-Perception
• Tech report: https://arxiv.org/pdf/2603.27365
• Perception model: tiiuae/falcon-perception
• vLLM/Docker: https://ghcr.io/tiiuae/falcon-ocr:latest

Quickstart

Installation

pip install "torch>=2.5" transformers pillow einops

Falcon OCR requires PyTorch 2.5 or newer for FlexAttention. The first call may be slower as torch.compile builds optimized kernels.

Single-Image OCR

import torch
from PIL import Image
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
    "tiiuae/Falcon-OCR",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
image = Image.open("document.png")
texts = model.generate(image)  # default category is "plain"
print(texts[0])

Choose an output format with category

texts = model.generate(image, category="text")     # plain text
texts = model.generate(image, category="formula")  # LaTeX
texts = model.generate(image, category="table")    # HTML table

API

model.generate(images, category="plain", **kwargs)

• Inputs:
  • images: a PIL.Image.Image or a list of images
  • category: one of plain, text, table, formula, caption, footnote, list-item, page-footer, page-header, section-header, title
• Returns: list[str], one extracted string per image

Layout OCR (Two-Stage Pipeline)

For sparse documents, running OCR on the whole image can work well. For dense documents with heterogeneous regions (multi-column layouts, interleaved tables and formulas, small captions), we provide an optional two-stage pipeline:

1. A layout detector finds regions on the page.
2. Falcon OCR runs independently on each crop with a category-specific prompt. We use PP-DocLayoutV3 as the layout detector.

results = model.generate_with_layout(image)
for det in results[0]:
    print(f"[{det['category']}] {det['text'][:100]}...")

Batch mode:

results = model.generate_with_layout(
    [Image.open("page1.png"), Image.open("page2.png")],
    ocr_batch_size=32,
)

The layout model is loaded lazily on the first generate_with_layout() call and runs on the same GPU as the OCR model. Returns: list[list[dict]], one list per image, in reading order:

{
    "category": "text",       # layout category
    "bbox": [x1, y1, x2, y2], # in original image pixels
    "score": 0.93,            # detection confidence
    "text": "..."             # extracted text
}

When to Use What

Mode	Best for	How
Plain OCR	Simple documents, real-world photos, slides, receipts, invoices	model.generate(image)
Layout + OCR	Complex multi-column documents, academic papers, reports, dense pages like newspapers	model.generate_with_layout(image)

Benchmark Results

olmOCR Benchmark

Category-wise performance comparison of FalconOCR against state-of-the-art OCR models. We report accuracy (%) across all category splits.

Model	Average	ArXiv Math	Base	Hdr/Ftr	TinyTxt	MultCol	OldScan	OldMath	Tables
Mistral OCR 3	81.7	85.4	99.9	93.8	88.9	82.1	48.8	68.3	86.1
Chandra	82.0	81.4	99.8	88.8	91.9	82.9	49.2	73.6	88.2
Gemini 3 Pro	80.2	70.6	99.8	84.0	90.3	79.2	47.5	84.9	84.9
PaddleOCR VL 1.5	79.3	85.4	98.8	96.9	80.8	82.6	39.2	66.4	84.1
PaddleOCR VL	79.2	85.4	98.6	96.9	80.8	82.5	38.8	66.4	83.9
DeepSeek OCR v2	78.8	81.9	99.8	95.6	88.7	83.6	33.7	68.8	78.1
Gemini 3 Flash	77.5	66.5	99.8	83.8	88.2	73.7	46.0	85.8	75.9
GPT 5.2	69.8	61.0	99.8	75.6	62.2	70.2	34.6	75.8	79.0
FalconOCR	80.3	80.5	99.5	94.0	78.5	87.1	43.5	69.2	90.3

OmniDocBench

Performance comparison on full-page document parsing. Overall↑ aggregates the three sub-metrics. Edit↓ measures text edit distance (lower is better). CDM↑ evaluates formula recognition accuracy. TEDS↑ measures table structure similarity.

Model	Overall↑	Edit↓	CDM↑	TEDS↑
PaddleOCR VL 1.5	94.37	0.025	94.4	91.1
PaddleOCR VL	91.76	0.024	91.7	85.9
Chandra	88.97	0.046	88.1	89.5
DeepSeek OCR v2	87.66	0.037	89.2	77.5
GPT 5.2	86.56	0.061	88.0	77.7
Mistral OCR 3	85.20	0.053	84.3	76.1
FalconOCR	88.64	0.055	86.8	84.6

Results Analysis

First, a compact model can be competitive when the interface is simple and the training signal is targeted. On olmOCR, Falcon OCR performs strongly on multi-column documents and tables, and is competitive overall against substantially larger systems. Second, evaluation on full-page parsing is sensitive to matching and representation details. On OmniDocBench, the table and formula metrics depend not only on recognition quality but also on how predicted elements are matched to ground truth and how output structure is normalized.

More broadly, these results suggest that an early-fusion single-stack Transformer can be a viable alternative to the common "vision encoder plus text decoder" recipe for OCR. We do not view this as a finished answer, but as a promising direction: one early-fusion backbone, a shared parameter space between text and images, a single decoding interface, and better data and training signals, rather than increasingly complex pipelines. To our knowledge, this is among the first demonstrations that this early-fusion recipe can reach competitive document OCR accuracy at this scale, and we hope it encourages further work in this direction.

Serving Throughput

Measured on a single A100-80GB GPU with vLLM, processing document images from olmOCR-Bench under high concurrency for optimal vLLM utilization.

• Layout + OCR — The full end-to-end pipeline: layout detection finds regions on each page, crops them, and vLLM runs OCR on every crop. This represents the real-world serving throughput, inclusive of both layout detection and OCR time.

Mode	tok/s	img/s	Description
Layout + OCR	5,825	2.9	Full pipeline: layout detection → crop → per-region OCR

At 0.3B parameters, Falcon OCR is roughly 3× smaller than 0.9B-class OCR VLMs (e.g., PaddleOCR VL), which translates directly into higher serving throughput at competitive accuracy.

Limitations

• Old scans and tiny text: Heavily degraded scans and very small glyphs remain challenging. These cases often require higher effective resolution and better coverage in the training mixture.
• Non-unique table representations: Visually identical tables can be encoded in structurally different HTML forms, which can affect tree-based metrics.
• Formula matching sensitivity: LaTeX and Unicode conventions can be penalized differently depending on the benchmark normalization and matching pipeline.

Examples

Click each section below to expand.

Handwriting and Real World Images

Tables

Formulas

Complex Layout

---

vLLM Server

We also provide a Docker-based vLLM-backed inference server capable of serving approximately 6,000 tokens per second.

Single Docker image with two services:

Service	Default Port	Description
vLLM	8000	Falcon-OCR vision-language model (OpenAI-compatible API)
Pipeline	5002	Full document parsing: layout detection → crop → OCR → markdown

The layout model runs inside the pipeline process — it is not a standalone service.

Quick Start

docker run -d --name falcon-ocr \
  --gpus '"device=0,1"' \
  -e EXPOSED_GPU_IDS=0,1 \
  -e VLLM_GPU=0 \
  -e PIPELINE_GPU=1 \
  -e VLLM_GPU_MEM_UTIL=0.90 \
  -p 8000:8000 \
  -p 5002:5002 \
  ghcr.io/tiiuae/falcon-ocr:latest

API

Health Checks

curl http://localhost:8000/health      # vLLM
curl http://localhost:5002/health      # Pipeline

Upload (multipart file upload — images and PDFs)

The easiest way to send files. Supports images and multi-page PDFs:

# Single image
curl -X POST http://localhost:5002/falconocr/upload \
  -F "files=@photo.jpg;type=image/jpeg"
# PDF document
curl -X POST http://localhost:5002/falconocr/upload \
  -F "files=@document.pdf;type=application/pdf"

Parse (full pipeline: layout + OCR)

Send base64-encoded images for layout detection, cropping, and OCR:

curl -X POST http://localhost:5002/falconocr/parse \
  -H "Content-Type: application/json" \
  -d '{
    "images": ["data:image/jpeg;base64,<...>"],
    "skip_layout": false
  }'

Response:

{
  "json_result": [[{
    "index": 0,
    "mapped_label": "text",
    "content": "The Manuscript",
    "bbox": [273, 273, 937, 380],
    "score": 0.3145
  }]],
  "markdown_result": "The Manuscript",
  "total_output_tokens": 93,
  "processing_time_ms": 414
}

Parse (direct VLM, no layout)

Skip layout detection and send the full image directly to the VLM:

curl -X POST http://localhost:5002/falconocr/parse \
  -H "Content-Type: application/json" \
  -d '{
    "images": ["data:image/jpeg;base64,<...>"],
    "skip_layout": true
  }'

Direct vLLM (OpenAI-compatible)

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "falcon-ocr",
    "messages": [{"role": "user", "content": [
      {"type": "image_url", "image_url": {"url": "data:image/png;base64,<...>"}},
      {"type": "text", "text": "Extract the text content from this image.\n<|OCR_PLAIN|>"}
    ]}],
    "max_tokens": 2048
  }'

Configuration

All settings are controlled via environment variables at docker run time.

GPU Assignment

Variable	Default	Description
VLLM_GPU	0	Host GPU ID for the vLLM process
PIPELINE_GPU	0	Host GPU ID for the pipeline (layout model)
EXPOSED_GPU_IDS	(all visible)	Comma-separated host GPU IDs passed via --gpus (for index remapping)

Port Assignment

Variable	Default	Description
VLLM_PORT	8000	Port for the vLLM OpenAI-compatible API
PIPELINE_PORT	5002	Port for the pipeline API

vLLM Tuning

Variable	Default	Description
VLLM_GPU_MEM_UTIL	0.90	Fraction of GPU memory vLLM can use
MAX_NUM_SEQS	2048	Max concurrent sequences in vLLM
MAX_MODEL_LEN	8192	Max model context length
DTYPE	bfloat16	Model dtype
MAX_NUM_BATCHED_TOKENS	(auto)	Max batched tokens per iteration
CHUNKED_PREFILL	false	Enable chunked prefill

Layout Model Tuning

Variable	Default	Description
LAYOUT_BATCH_SIZE	64	Batch size for layout detection inference

Model Paths

Variable	Default	Description
FALCON_OCR_MODEL	/models/Falcon-OCR	Path to Falcon-OCR VLM weights (inside container)
SERVED_MODEL_NAME	falcon-ocr	Model name exposed by vLLM API

Deployment Modes

Two GPUs (best throughput)

vLLM on one GPU, layout model on another — zero GPU contention:

docker run -d --name falcon-ocr \
  --gpus '"device=3,4"' \
  -e EXPOSED_GPU_IDS=3,4 \
  -e VLLM_GPU=3 \
  -e PIPELINE_GPU=4 \
  -e VLLM_GPU_MEM_UTIL=0.90 \
  -p 8000:8000 \
  -p 5002:5002 \
  ghcr.io/tiiuae/falcon-ocr:latest

Single GPU (memory sharing)

Both services share one GPU — tune VLLM_GPU_MEM_UTIL to leave room for the layout model:

docker run -d --name falcon-ocr \
  --gpus '"device=0"' \
  -e EXPOSED_GPU_IDS=0 \
  -e VLLM_GPU=0 \
  -e PIPELINE_GPU=0 \
  -e VLLM_GPU_MEM_UTIL=0.55 \
  -e LAYOUT_BATCH_SIZE=32 \
  -e MAX_NUM_SEQS=512 \
  -p 8000:8000 \
  -p 5002:5002 \
  ghcr.io/tiiuae/falcon-ocr:latest

Custom Ports

docker run -d --name falcon-ocr \
  --gpus '"device=0,1"' \
  -e EXPOSED_GPU_IDS=0,1 \
  -e VLLM_GPU=0 \
  -e PIPELINE_GPU=1 \
  -e VLLM_PORT=18000 \
  -e PIPELINE_PORT=15002 \
  -p 18000:18000 \
  -p 15002:15002 \
  ghcr.io/tiiuae/falcon-ocr:latest

Docker --gpus "device=3,4" makes the container see GPUs as local indices 0,1. EXPOSED_GPU_IDS=3,4 allows you to reference host GPU IDs (VLLM_GPU=3, PIPELINE_GPU=4); the entrypoint remaps them to the correct container-local indices.

Citation

If you use Falcon OCR, please cite:

@article{bevli2026falcon,
  title   = {Falcon Perception},
  author  = {Bevli, Aviraj and Chaybouti, Sofian and Dahou, Yasser and Hacid, Hakim and Huynh, Ngoc Dung and Le Khac, Phuc H. and Narayan, Sanath and Para, Wamiq Reyaz and Singh, Ankit},
  journal = {arXiv preprint arXiv:2603.27365},
  year    = {2026},
  url     = {https://arxiv.org/abs/2603.27365}
}