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--- |
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license: apache-2.0 |
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datasets: |
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- allenai/olmOCR-mix-0225 |
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- prithivMLmods/Opendoc1-Analysis-Recognition |
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- prithivMLmods/Opendoc2-Analysis-Recognition |
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- prithivMLmods/Openpdf-Analysis-Recognition |
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pipeline_tag: image-text-to-text |
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tags: |
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- OCR |
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- Pdf |
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- Doc |
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- Image |
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- text-generation-inference |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-VL-7B-Instruct |
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library_name: transformers |
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--- |
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# **docscopeOCR-7B-050425-exp** |
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> 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. |
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# Key Enhancements |
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* **Advanced Document-Level OCR**: Capable of extracting structured content from complex, multi-page documents such as invoices, academic papers, forms, and scanned reports. |
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* **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. |
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* **State-of-the-Art Performance Across Resolutions**: Achieves competitive results on OCR and visual QA benchmarks such as DocVQA, MathVista, RealWorldQA, and MTVQA. |
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* **Video Understanding up to 20+ minutes**: Supports detailed comprehension of long-duration videos for content summarization, Q\&A, and multi-modal reasoning. |
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* **Visually-Grounded Device Interaction**: Enables mobile/robotic device operation via visual inputs and text-based instructions using contextual understanding and decision-making logic. |
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# Quick Start with Transformers |
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```python |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/docscopeOCR-7B-050425-exp", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("prithivMLmods/docscopeOCR-7B-050425-exp") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", |
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}, |
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{"type": "text", "text": "Describe this image."}, |
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], |
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} |
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] |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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## Training Details |
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| Parameter | Value | |
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|-------------------------|-----------------------------------------------------| |
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| **Dataset Size** | 274,209 samples (Modular Combination of Datasets) | |
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| **Model Architecture** | `Qwen2_5_VLForConditionalGeneration` | |
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| **Hardware** | 2 × NVIDIA A100 SXM (32 vCPUs) | |
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| **Total Disk** | 170,000 MB | |
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| **Training Time** | 9,020 seconds (~2.51 hours) | |
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| **Learning Rate** | 1e-5 | |
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| **Scheduler** | Linear Decay | |
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| **Warmup Steps** | 750 | |
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| **Precision** | bfloat16 | |
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> [!note] |
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> The open dataset image-text response will be updated soon. |
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# Intended Use |
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This model is intended for: |
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* High-fidelity OCR from documents, forms, receipts, and printed or scanned materials. |
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* Image and document-based question answering for educational and enterprise applications. |
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* Extraction and LaTeX formatting of mathematical expressions from printed or handwritten content. |
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* Retrieval and summarization from long documents, slides, and multi-modal inputs. |
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* Multilingual OCR and structured content extraction for global use cases. |
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* Robotic or mobile automation with vision-guided contextual interaction. |
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# Limitations |
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* May show degraded performance on extremely low-quality or occluded images. |
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* Not optimized for real-time applications on low-resource or edge devices due to computational demands. |
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* Variable accuracy on uncommon or low-resource languages/scripts. |
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* Long video processing may require substantial memory and is not optimized for streaming applications. |
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* Visual token settings affect performance; suboptimal configurations can impact results. |
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* In rare cases, outputs may contain hallucinated or contextually misaligned information. |
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## References |
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- **DocVLM: Make Your VLM an Efficient Reader** |
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[https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1) |
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- **YaRN: Efficient Context Window Extension of Large Language Models** |
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[https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071) |
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- **Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution** |
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[https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191) |
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- **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond** |
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[https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966) |
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- **A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy** |
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[https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210) |
