EpigraphiX-AI: Neural Epigraphical OCR, Topological Binarization, Strict Manuscript Authentication, and Multi-Model Intelligence for Degraded Historical Palm-Leaf Manuscripts

Abstract

Historical South Indian palm-leaf manuscripts (Thaliyola), inscribed using incised iron styluses (Ezhanithandu), preserve invaluable ancient Sanskrit, Grantha, and Old Malayalam treatises spanning Ayurveda, astronomy, mathematics, and philosophy. However, severe physical degradation—including high-frequency cellulose fiber striations, biological decay, uneven stylus incision depths, and carbon ink dispersion loss—renders conventional OCR engines ineffective. In this paper, we present EpigraphiX-AI, an end-to-end epigraphical intelligence suite and neural optical character recognition architecture. Our framework introduces six core scientific innovations: (1) an automated Multi-Gamut Strict Palm-Leaf Authenticity & Spatial Localization engine that rejects non-manuscripts (human portraits, outdoor/indoor scenes, digital UI) while detecting authentic folios across diverse lighting and mounting conditions; (2) Fiber-Aware Neural Inpainting (FANI 2.0) with 3D Photometric Stereo (PTM) surface simulation to isolate stylus incisions from fibrous wood grain textures; (3) an O(1) Integral-Image Adaptive Sauvola Binarization algorithm operating at sub-3.8ms latency; (4) Persistent Homology Betti Filtration (β₀, β₁) for topological loop preservation in complex Grantha ligatures; (5) a 5-Model Epigraphical Decision Space benchmarked across Support Vector Machines (SVM), Random Forest, Gaussian Naive Bayes, k-Nearest Neighbors, and Convolutional Neural Lattices; and (6) a Multilingual Semantic Translation & Sandhi Grammar Engine with real-time dynamic canvas operations. Experimental evaluation on an archival corpus of 1,250 historical palm-leaf folios demonstrates that EpigraphiX-AI achieves a Word Accuracy Rate (WAR) of 97.4%, Character Accuracy of 98.6%, Character Error Rate (CER) of 1.4%, Precision of 98.6%, Recall of 98.4%, Specificity of 99.4%, and Palm Authentication Accuracy of 99.4%, substantially outperforming state-of-the-art baselines.