Haowen Lin
Abstract
Historical maps are important resources for various kinds of studies, providing insights for natural science and social science studies such as biology, landscape changes, and history [1]. However, text recognition on maps remains a challenging task because map usually has a complex background in which textual content appears in numerous colors, fonts, sizes, and orientations. Even if we were able to acquire perfectly recognized words and characters automatically, it is still difficult to generate useful information because individual words are not meaningful. For example, a typical result from OCR scanning or manual map digitization is that each recognized bounding box only contains a single word (Figure 1).
- YY. Chiang. Unlocking Textual Content from Historical Maps-Potentials and Applications, Trends, and Outlooks. International Conference on Recent Trends in Image Processing and Pattern Recognition, pages 111--124. Springer, 2016.Google Scholar
- M. Heliński, K. Miłosz, and P. Tomasz. Report on the comparison of Tesseract and ABBYY FineReader OCR engines. 2012.Google Scholar
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