Digital Preservation of Indian Knowledge Systems Using Artificial Intelligence: A Comprehensive Framework

Vollständiger Abstract

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Indian Knowledge Systems (IKS) encompass a large body of intellectual, scientific, medical, linguistic, philosophical, mathematical, astronomical, literary and cultural knowledge preserved through manuscripts and related documentary traditions. A substantial part of this heritage exists on palm leaves, paper, birch bark and other materials whose physical deterioration can continue even after digitization. Faded ink, stains, bleed-through, uneven illumination, surface damage, low resolution and missing character strokes reduce the usefulness of digital images for both human reading and automated text recognition. This paper develops and elaborates an artificial intelligence (AI)-assisted framework for digital preservation of degraded Indian manuscripts. The framework combines repository-based acquisition, metadata and provenance capture, image-quality assessment, conventional preprocessing, degradation-aware deep-learning restoration, selective generative inpainting, super-resolution, optical character recognition (OCR), quantitative evaluation and expert validation. The experimental component described in the original study uses approximately 100 manuscript images collected from open-access digital collections. Reported pilot results indicate SSIM improvements of approximately 0.05–0.10, PSNR improvements of 2–6 dB, contrast improvements of 20–35%, noise reduction of 20–35%, edge-preservation improvement of 15–30% and OCR accuracy improvement of approximately 10–20 percentage points. Character Error Rate (CER) and Word Error Rate (WER) were reported to decrease by approximately 10–25% and 10–30%, respectively, with character recognition accuracy reaching approximately 85–90%. These results are interpreted as evidence that enhancement can improve machine readability and access, while not constituting proof that AI-generated pixels are historically correct. The expanded framework therefore separates original evidence, algorithmically enhanced representations, and reconstructed content; preserves processing provenance and places scholars in the validation loop. The paper also situates the framework within recent developments in Indian manuscript digitization, historical document analysis, trustworthy AI and the Gyan Bharatam initiative. The central argument is that AI should be deployed as an assistive preservation technology that increases discoverability and research access without replacing the original manuscript or scholarly judgement.