typographicalOCR

Typographical OCR is a branch of optical character recognition focused on preserving typographic information in digitized documents. Unlike conventional OCR, which primarily aims to produce plain text, typographical OCR seeks to identify and encode font styles, sizes, weights, spacing, and layout features in addition to the textual content. The goal is to enable faithful rendering, typographic search, and scholarly analysis of typography and publishing history.

The approach combines document image analysis with character recognition and font-aware modeling. Typical steps include preprocessing to improve scan quality; page layout analysis to segment blocks, columns, and text lines; font identification and glyph reconstruction to map scanned shapes to character codes; optical character recognition to transcribe the text; and post-processing that uses dictionaries, linguistic models, and typographic constraints to resolve ligatures, hyphenation, and ornamental characters. Output can be plain text accompanied by metadata or richly structured formats such as XML/TEI or HTML with CSS that encode font families, sizes, weights, and layout information. Some implementations generate PDFs with embedded font hints or CSS for faithful rendering.

Applications include digitization of books and journals, archival preservation of historical typography, bibliographic research, and improving accessibility with layout-aware text rendering. Challenges remain in handling historical and decorative fonts, multi-column layouts, ligatures, diacritics, and degraded or color-printed materials. Evaluation typically considers transcription accuracy (CER/WER) and fidelity of typography metadata.