Enterprise AI Analysis
OBIFIo-SAM: multi-task semantic recognition and segmentation of oracle bone inscription
OBIFIo-SAM is a novel multi-task framework integrating Florence-2 and SAM for high-precision semantic recognition and segmentation of Oracle Bone Inscriptions (OBI). It addresses low-resource and complex background challenges using weighted Focal Loss and an OracleVQA dataset, outperforming existing LLMs and establishing a robust pathway for digital preservation and automated interpretation of ancient scripts.
Key Performance Indicators
OBIFlo-SAM delivers unparalleled accuracy and efficiency across critical tasks, driving significant advancements in ancient script analysis and cultural heritage preservation.
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Top-1 OBI Recognition Accuracy
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Zero-shot Segmentation IoU
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Object Detection mAP50
Deep Analysis & Enterprise Applications
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OBIFlo-SAM: A Multi-Task Collaborative Framework
OBIFlo-SAM integrates the Florence-2 model for comprehensive vision-language understanding and the Segment Anything Model (SAM) for high-fidelity segmentation, creating a robust framework for Oracle Bone Inscription (OBI) analysis. This architecture enables semantic interpretation, object detection, and pixel-level boundary delineation, addressing challenges in noisy, low-resource scenarios. By reforming all visual tasks into sequence generation, it provides a unified approach for digital preservation and automated archaeological documentation of ancient scripts.
91.1%
Peak Validation Accuracy (Partial Fine-tuning)
Optimized Fine-tuning with Weighted Focal Loss
The proposed methodology employs a weighted Focal Loss strategy for fine-tuning the pre-trained Florence-2 model. This method is specifically engineered to improve recognition of low-frequency and easily confusable characters in OBI images. Experimental results showed that partial fine-tuning achieved a peak validation accuracy of 91.1% at epoch 21, significantly surpassing the full fine-tuning approach by over 5 percentage points and demonstrating superior asymptotic performance.
Advanced Zero-Shot OBI Image Segmentation
The Florence-2 model alone exhibits limitations in segmenting noisy OBI images, with an Intersection over Union (IoU) of only 57.35%. OBIFlo-SAM, through its integration with the SAM module, significantly enhances segmentation performance. This integrated approach boosts IoU to 84.66%, Dice Similarity Coefficient (DSC) to 90.32%, and AUC to 94.36%, enabling precise character boundary extraction even in complex backgrounds.
| Metric | Florence (Baseline) | OBIFlo-SAM (Ours) |
|---|---|---|
| IoU (%) | 57.35 | 84.66 |
| DSC (%) | 68.91 | 90.32 |
| AUC (%) | 85.90 | 94.36 |
Superior OBI Semantic Recognition
OBIFlo demonstrates superior performance in semantic recognition of Oracle Bone Inscriptions compared to various existing methods. While models like CycleGAN and CLIP show no capability, and GPT-4o achieves only 3% Top-1 accuracy, OBIFlo achieves a Top-1 accuracy of 91.1% and a Top-10 accuracy of 92.8%. This robust performance is maintained across the frequency spectrum, including rare characters, effectively addressing class imbalance issues.
| Method | Top-1@Acc (%) | Top-10@Acc (%) |
|---|---|---|
| CycleGAN | 0 | 0 |
| CLIP | 0 | 0 |
| GPT-4o | 3 | 5 |
| Chinese-CLIP | 89.5 | 91.3 |
| Florence-CEloss | 88.9 | 90.3 |
| OBIFlo (Ours) | 91.1 | 92.8 |
Enterprise Process Flow for OBI Digitization
The OBIFlo-SAM framework streamlines the digital preservation and interpretation of Oracle Bone Inscriptions through a multi-stage process, from data acquisition to semantic interpretation. It leverages advanced AI for robust character recognition and pixel-level segmentation, ensuring high-fidelity outputs for cultural heritage and scholarly research.
Enterprise Process Flow
Data Acquisition & Preprocessing
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Florence-2 Fine-tuning (Semantic & Detection)
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Object Detection (Bounding Boxes)
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SAM-guided Zero-shot Segmentation (Pixel Masks)
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High-fidelity OBI Character Output
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Digital Preservation & Scholarly Analysis
Advanced ROI Calculator
Estimate the transformative impact of advanced AI on your enterprise's data analysis and heritage preservation efforts, particularly in the domain of ancient script digitization and cultural heritage preservation.
Estimated Annual Savings
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Annual Hours Reclaimed
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Implementation Roadmap
A phased approach ensures seamless integration and maximum impact for your organization, leveraging OBIFlo-SAM to enhance your cultural heritage preservation initiatives.
Phase 1: Discovery & Customization
Initial workshops to understand your specific OBI datasets and integration needs. Customization of OBIFlo-SAM models for your unique character sets and quality requirements.
Phase 2: Data Integration & Model Training
Seamless integration of your OBI rubbing images and existing annotations. Iterative fine-tuning of OBIFlo-SAM using your data for optimal performance in semantic recognition, detection, and segmentation.
Phase 3: System Deployment & Validation
Deployment of the OBIFlo-SAM framework within your infrastructure. Rigorous validation against real-world OBI samples to ensure high accuracy and robustness across diverse conditions.
Phase 4: Ongoing Support & Enhancement
Continuous monitoring, performance optimization, and updates to adapt to evolving research needs and new OBI discoveries. Training and support for your team to maximize system utilization.
Unlock the Future of Cultural Heritage AI
Ready to transform your enterprise with cutting-edge AI? Schedule a personalized consultation to explore how OBIFlo-SAM can benefit your organization's cultural heritage preservation and research endeavors.
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