We combine three methods which significantly improve the OCR accuracy of OCR
models trained on early printed books: (1) The pretraining method utilizes the
information stored in already existing models trained on a variety of typesets
(mixed models) instead of starting the training from scratch. (2) Performing
cross fold training on a single set of ground truth data (line images and their
transcriptions) with a single OCR engine (OCRopus) produces a committee whose
members then vote for the best outcome by also taking the top-N alternatives
and their intrinsic confidence values into account. (3) Following the principle
of maximal disagreement we select additional training lines which the voters
disagree most on, expecting them to offer the highest information gain for a
subsequent training (active learning). Evaluations on six early printed books
yielded the following results: On average the combination of pretraining and
voting improved the character accuracy by 46% when training five folds starting
from the same mixed model. This number rose to 53% when using different models
for pretraining, underlining the importance of diverse voters. Incorporating
active learning improved the obtained results by another 16% on average
(evaluated on three of the six books). Overall, the proposed methods lead to an
average error rate of 2.5% when training on only 60 lines. Using a substantial
ground truth pool of 1,000 lines brought the error rate down even further to
less than 1% on average.Comment: Submitted to JLCL Volume 33 (2018), Issue 1: Special Issue on
Automatic Text and Layout Recognitio
