docscopeOCR-7B-050425-exp/README.md

---
license: apache-2.0
datasets:
- allenai/olmOCR-mix-0225
- prithivMLmods/Opendoc1-Analysis-Recognition
- prithivMLmods/Opendoc2-Analysis-Recognition
- prithivMLmods/Openpdf-Analysis-Recognition
pipeline_tag: image-text-to-text
tags:
- OCR
- Pdf
- Doc
- Image
- text-generation-inference
- KIE-Key Information Extraction
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
---

![22.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/_L1v41LZYfOQCLLwHtAEy.png)

# **docscopeOCR-7B-050425-exp**

> The **docscopeOCR-7B-050425-exp** model is a fine-tuned version of **Qwen/Qwen2.5-VL-7B-Instruct**, optimized for **Document-Level Optical Character Recognition (OCR)**, **long-context vision-language understanding**, and **accurate image-to-text conversion with mathematical LaTeX formatting**. Built on top of the Qwen2.5-VL architecture, this model significantly improves document comprehension, structured data extraction, and visual reasoning across diverse input formats.

# Key Enhancements

* **Advanced Document-Level OCR**: Capable of extracting structured content from complex, multi-page documents such as invoices, academic papers, forms, and scanned reports.

* **Enhanced Long-Context Vision-Language Understanding**: Designed to handle dense document layouts, long sequences of embedded text, tables, and diagrams with coherent cross-reference understanding.

* **State-of-the-Art Performance Across Resolutions**: Achieves competitive results on OCR and visual QA benchmarks such as DocVQA, MathVista, RealWorldQA, and MTVQA.

* **Video Understanding up to 20+ minutes**: Supports detailed comprehension of long-duration videos for content summarization, Q\&A, and multi-modal reasoning.

* **Visually-Grounded Device Interaction**: Enables mobile/robotic device operation via visual inputs and text-based instructions using contextual understanding and decision-making logic.

# Quick Start with Transformers

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/docscopeOCR-7B-050425-exp", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/docscopeOCR-7B-050425-exp")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```

## Training Details

| Parameter               | Value                                               |
|-------------------------|-----------------------------------------------------|
| **Dataset Size**        | 274,209 samples (Modular Combination of Datasets)   |
| **Model Architecture**  | `Qwen2_5_VLForConditionalGeneration`                |
| **Hardware**            | 2 × NVIDIA A100 SXM (32 vCPUs)                      |
| **Total Disk**          | 170,000 MB                                          |
| **Training Time**       | 9,020 seconds (~2.51 hours)                         |
| **Learning Rate**       | 1e-5                                                |
| **Scheduler**           | Linear Decay                                        |
| **Warmup Steps**        | 750                                                 |
| **Precision**           | bfloat16                                            |

> [!note]
> The open dataset image-text response will be updated soon.

# Intended Use

This model is intended for:

* High-fidelity OCR from documents, forms, receipts, and printed or scanned materials.
* Image and document-based question answering for educational and enterprise applications.
* Extraction and LaTeX formatting of mathematical expressions from printed or handwritten content.
* Retrieval and summarization from long documents, slides, and multi-modal inputs.
* Multilingual OCR and structured content extraction for global use cases.
* Robotic or mobile automation with vision-guided contextual interaction.

# Limitations

* May show degraded performance on extremely low-quality or occluded images.
* Not optimized for real-time applications on low-resource or edge devices due to computational demands.
* Variable accuracy on uncommon or low-resource languages/scripts.
* Long video processing may require substantial memory and is not optimized for streaming applications.
* Visual token settings affect performance; suboptimal configurations can impact results.
* In rare cases, outputs may contain hallucinated or contextually misaligned information.


## References

- **DocVLM: Make Your VLM an Efficient Reader** 
  [https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1)

- **YaRN: Efficient Context Window Extension of Large Language Models**  
  [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)

- **Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution**  
  [https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191)

- **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**  
  [https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966)

- **A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy**
  [https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210)