---
license: apache-2.0
base_model:
- tiiuae/Falcon-OCR
pipeline_tag: image-to-text
---
<p align="center">
  <img src="logo.png" alt="logo" width="500">
</p>


<div align="center">
  <a href="https://huggingface.co/OrionLLM/GRM-OCR/" style="text-decoration: none;">
    <img src="https://img.shields.io/badge/🤗-HuggingFace-FC926C?style=for-the-badge" alt="HuggingFace">
  </a>
  <a href="https://www.apache.org/licenses/LICENSE-2.0" style="text-decoration: none;">
    <img src="https://img.shields.io/badge/📜-License-E343BD?style=for-the-badge" alt="License">
  </a>
</div>

---

## 1. Introduction

**GRM-OCR** is a **300M-parameter OCR model** built for **document and image text extraction**, optimized for **efficiency and deployability** without sacrificing recognition quality. It is designed to deliver strong OCR performance at a compact scale, making it practical for local inference, edge deployment, and high-throughput serving pipelines.

Built on top of [tiiuae/Falcon-OCR](https://huggingface.co/tiiuae/Falcon-OCR), GRM-OCR inherits the early-fusion single-stack Transformer architecture and extends it with improved training signals, broader document coverage, and refinements targeted at real-world document diversity.

GRM-OCR is ideal for users who need **reliable, fast, and resource-efficient OCR** across a wide range of document types — from scanned pages and academic papers to invoices, receipts, handwritten notes, and complex multi-column layouts.


## 2. Key Capabilities

- **Compact and Efficient:** At just **300M parameters**, GRM-OCR is roughly 3× smaller than comparable OCR VLMs, translating directly into faster inference and lower memory requirements.
- **Layout-Aware Pipeline:** Optional two-stage layout detection + per-region OCR for dense, multi-column, and heterogeneous documents.
- **Early-Fusion Architecture:** A single Transformer backbone processes text and images in a shared parameter space, avoiding the complexity of separate encoder-decoder pipelines.
- **Broad Document Coverage:** Handles handwriting, real-world photos, academic papers, tables, formulas, headers, captions, and more.
- **vLLM Compatible:** Serves efficiently via vLLM with an OpenAI-compatible API.


## 3. Quickstart

### Installation

```bash
pip install "torch>=2.5" transformers pillow einops
```

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

### Single-Image OCR

```python
import torch
from PIL import Image
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "OrionLLM/GRM-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`

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

## 4. API

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

| Parameter | Type | Description |
|-----------|------|-------------|
| `images` | `PIL.Image.Image` or `list` | One or more input images |
| `category` | `str` | Output format: `plain`, `text`, `table`, `formula`, `caption`, `footnote`, `list-item`, `page-footer`, `page-header`, `section-header`, `title` |

**Returns:** `list[str]` — one extracted string per image.

---

## 5. Layout OCR (Two-Stage Pipeline)

For dense documents with heterogeneous regions (multi-column layouts, interleaved tables and formulas, small captions), GRM-OCR supports an optional two-stage pipeline:

1. A layout detector identifies regions on the page.
2. GRM-OCR runs independently on each crop with a category-specific prompt.

We use [PP-DocLayoutV3](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3_safetensors) as the layout detector.

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

**Batch mode:**

```python
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 device as the OCR model.

**Returns:** `list[list[dict]]`, one list per image, in reading order:

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


## 6. Architecture

GRM-OCR is built on the **Falcon-OCR early-fusion architecture** — a single-stack Transformer that processes both text and images within a shared parameter space, without a separate vision encoder. This design choice avoids the complexity of common "vision encoder + text decoder" pipelines and enables more coherent cross-modal reasoning.

GRM-OCR applies targeted training improvements on top of this foundation: broader document diversity, refined category-specific prompting, and improved alignment for real-world OCR conditions — resulting in a model that punches above its weight class on structured document tasks while remaining deployable even on modest consumer hardware.


---

<div align="center">

**GRM-OCR** is developed by **[OrionLLM](https://huggingface.co/OrionLLM)** and released under the Apache 2.0 License.

</div>