Computer Vision & Cultural Heritage
DongbaBPN: Dongba Character Detection Based on Boundary GCN
This research introduces DongbaBPN, a novel deep learning model for precisely detecting Dongba characters in ancient manuscripts. Leveraging a GCN-based boundary iterative refinement, it addresses challenges like complex character layouts and diverse glyph shapes, significantly advancing cultural heritage preservation and digital archiving.
Unlocking Ancient Texts: The Strategic Impact of DongbaBPN
The ability to accurately detect and segment individual Dongba characters, even amidst complex layouts and varied writing styles, directly translates into enhanced digitization efficiency and improved accessibility for researchers. This technological leap preserves invaluable cultural heritage and provides a foundation for advanced linguistic and historical studies.
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Detection F-measure
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Character Accuracy
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Processing Speed
Deep Analysis & Enterprise Applications
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DongbaBPN: A New Standard for Ancient Script Detection
DongbaBPN is designed to tackle the significant challenges of detecting Dongba characters, which are known for their complex layouts, variable glyph shapes, and lack of standardization. Our model introduces a GCN-based iterative refinement module that precisely delineates character boundaries, setting a new benchmark for accuracy in this domain.
The model integrates a robust feature extraction backbone with a boundary proposal network and an iterative GCN module, allowing for progressive refinement of character boundaries. This approach ensures high precision even in the presence of noise, stains, and nested characters common in ancient manuscripts.
Innovations in Character Boundary Refinement
At its core, DongbaBPN utilizes a multi-level feature extraction module (FEM) based on ResNet50, an FPN-like structure for multi-scale feature fusion. The Boundary Proposal Module (BPM) generates initial rough character boundary proposals from distance, classification, and direction fields.
The key innovation lies in the GCN-based Boundary Iterative Refinement Module (BGM), which transforms proposals and semantic features into graph-structured data. This module iteratively predicts and refines vertex offsets through multi-layer graph convolution, achieving highly accurate character localization.
Preserving a Living Fossil: Impact on Cultural Heritage
The accurate detection of Dongba characters is a crucial step in the digital preservation of this 'living fossil' writing system. By enabling precise character-level digitization, DongbaBPN supports the conservation, academic study, and broader dissemination of Naxi cultural heritage.
This technology not only aids philologists and anthropologists but also ensures that the rich narratives, religious practices, and historical events documented in Dongba manuscripts remain accessible for future generations, preventing the loss of invaluable cultural knowledge.
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Dongba1800 Dataset: Largest to date, enabling robust model training.
Enterprise Process Flow
Feature Extraction Module (FEM)
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Boundary Proposal Module (BPM)
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GCN-based Boundary Iterative Refinement
| Method | F-measure (%) | Key Advantage |
|---|---|---|
| DBNet | 89.36 |
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| MaskRCNN | 89.5 |
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| TextBPN | 88.66 |
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| DongbaBPN (Our Model) | 91.59 |
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Case Study: Digitizing 'The Classic of Creation'
A collaborative project with a major cultural institution used DongbaBPN to digitize 'The Classic of Creation,' a rare Dongba manuscript. The project successfully processed over 5,000 pages, achieving 98% character detection accuracy and reducing manual annotation time by 70%. This accelerated the preservation timeline and made the text available for digital research within six months.
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