Ancient Sanskrit manuscripts on ochre parchment represent a critical challenge for Document Image Analysis. Standard binarisation methods conflate chromatic staining with ink, leading to topological loss. We present MorphBG, a pipeline using per-channel morphological background estimation to preserve ink signals. Unlike recent deep learning methods, MorphBG is a classical, CPU-efficient approach. We demonstrate its scalability by processing over 1,200 manuscript pages. While MorphBG achieves a DRD of 2.38, zero-shot OCR engines yield 0% accuracy on this script. Our current Work-in-Progress (WIP) involves the VedOCR Dataset: an expert-annotated resource of 32,000 archaic character instances to bridge the domain gap in Sanskrit recognition.