Historical maps represent rich cultural and linguistic artefacts, yet their textual components remain underexplored in digitization initiatives. This study investigates the integration of Optical Character Recognition (OCR) and Natural Language Processing (NLP) methods into a broader digitization project at a university library, aimed at improving accessibility and scholarly engagement with a large cartographic collection. The project combined a user-friendly map portal with computational workflows to extract, transcribe, and analyze textual elements such as place names, legends, and paratextual data. A multi-stage workflow was developed, incorporating text segmentation, semi-automatic transcription, NLP-based categorization, and corpus-based analysis. Several open-source OCR tools including eScriptorium, PERO OCR, Tesseract, OCR4all, and OCRmyFiles were evaluated for recognition accuracy, handling of heterogeneous layouts and typefaces, and adaptability for manual correction. Challenges encountered include uneven text distribution, variable orientation, low contrast, overlapping graphical elements, and damaged sources, all of which constrained automated recognition. Segmentation quality proved a critical bottleneck, emphasizing the need for hybrid approaches that combine automation with targeted manual intervention. Corpus-based visualizations using Voyant Tools supported exploratory analysis, comparative evaluation of OCR output, and metadata enrichment, though limitations remain in representing multi-word geographical terms. The study also explores incremental refinement of domain-specific transcription models to improve recognition performance. Overall, the research highlights both the potential and limits of automated text processing in digital cartography, advocating a pragmatic hybrid approach that treats cartographic text recognition as a selective, research-driven process rather than a fully scalable solution.