Delorme_1-OCR-7B-Post1.0

The Delorme_1-OCR-7B-Post1.0 model is a refined and optimized version of Gliese-OCR-7B-Post1.0, built upon the Qwen2.5-VL architecture. It represents the final iteration in the OCR series, offering enhanced efficiency, precision, and visualization capabilities for document OCR, visual analysis, and information extraction. Fine-tuned with extended document visualization data and OCR-focused objectives, this model delivers superior accuracy across a wide range of document types, including scanned PDFs, handwritten pages, structured forms, and analytical reports.

Key Enhancements

• Optimized Document Visualization and OCR Pipeline: Significantly improved recognition of text, layout, and embedded visuals for structured document understanding.
• Context-Aware Multimodal Linking: Enhanced understanding of document context with stronger alignment between text, images, and layout components.
• Refined Document Retrieval: Improved retrieval accuracy from complex layouts and multi-page documents.
• High-Fidelity Content Extraction: Precise extraction of structured, semi-structured, and unstructured information with advanced text normalization.
• Analytical Recognition: Superior reasoning over charts, graphs, tables, and mathematical equations.
• Improved Visual Reasoning and Layout Awareness: Trained on document visualization datasets for advanced spatial and semantic comprehension.
• State-of-the-Art Performance Across Resolutions: Achieves top results on benchmarks such as DocVQA, InfographicVQA, MathVista, and RealWorldQA.
• Extended Multimodal Duration Support: Handles long document sequences and extended videos (20+ minutes).
• Final Release Stability: Consolidates all prior improvements for stable and reliable performance.

Quick Start with Transformers

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

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Delorme_1-OCR-7B-Post1.0", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Delorme_1-OCR-7B-Post1.0")

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

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",
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=256)
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)

Intended Use

• Document visualization and OCR extraction tasks.
• Context-aware document retrieval and multimodal linking.
• Extraction and LaTeX formatting of equations and structured content.
• Analytical document interpretation (charts, tables, graphs, and figures).
• Multilingual OCR for enterprise, academic, and research use cases.
• Summarization, question answering, and cross-modal reasoning over long documents.
• Intelligent robotic or mobile automation guided by visual document input.

Limitations

• Reduced accuracy on heavily degraded or occluded documents.
• High computational requirements for large-scale or real-time applications.
• Limited optimization for low-resource or edge devices.
• Occasional misalignment in text layout or minor hallucinations in outputs.
• Performance may vary depending on visual token configuration and context length settings.