Unicode font is a globally recognized coding system that assigns a unique number to every character, regardless of the platform, program, or language. However, a significant fraction of Gujarati text documents available online follow legacy fonts which are non-Unicode standard. These documents are rendered unsearchable due to the use of custom-embedded non-Unicode font subsets. This study presents a novel approach for extracting Gujarati text from non-Unicode standard PDFs with embedded fonts, addressing the challenges posed by these legacy fonts and providing a pathway to convert and preserve such documents in a searchable, Unicode-compliant format. Using VGG16, features are extracted from images of glyphs taken from non-Unicode font files. Cosine similarity is used to compare these glyph images with a consolidated reference set, assigning text characters to each Unicode character in the non-Unicode font files based on the best matching image in the reference set. The reconstructed text from the proposed method was compared with State-of-the-Art OCR technologies, including Google Cloud Vision OCR and Tesseract OCR. The results demonstrated a substantial improvement, with the proposed method achieving error rates between 0–2% for the majority of pages, compared to 4–6% with Tesseract OCR and 6–13% with Google Vision OCR.