High-fidelity Epigraphic Rendering for Zonated Feature Extraction and Labelled Datasets
Reason this release was yanked:
Initial name reservation and metadata refinement; please use the forthcoming stable release for functional builds.
Project description
HERZFELD
High-fidelity Epigraphic Rendering for Zonated Feature Extraction and Labelled Datasets
HERZFELD is a specialised pipeline for generating synthetic datasets for Optical Character Recognition (OCR) of ancient inscriptions, specifically focusing on Middle Persian and Inscriptional Pahlavi.
Why HERZFELD?
Traditional 2D synthetic data (like flat PNG/JPG images) fails to capture the complexity of epigraphy. HERZFELD leverages Blender and Geometry Nodes to produce physically accurate 3D renders of stone surfaces.
Key Features:
- OpenEXR Output: Provides multi-channel "Ground Truth" data.
- Depth Maps: Allows AI models to distinguish between intentional carvings and natural stone erosion.
- Surface Normals: Facilitates training under variable raking-light conditions, mimicking field documentation.
- Procedural Rock Generation: Creates infinitely varied geological backgrounds for robust ML training.
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