Improving OCR of historical newspapers and journals published in Finland

Abstract

This paper presents experiments on Optical character recognition (OCR) of historical newspapers and journals published in Finland. The corpus has two main languages: Finnish and Swedish and is written in both Blackletter and Antiqua fonts. Here we experiment with how much training data is enough to train high accuracy models, and try to train a joint model for both languages and all fonts. So far we have not been successful in getting one best model for all, but it is promising that with the mixed model we get the best results on the Finnish test set with 95 % CAR, which clearly surpasses previous results on this data set.