  In this paper, we propose a data augmentation framework for Optical Character
Recognition (OCR). The proposed framework is able to synthesize new viewing
angles and illumination scenarios, effectively enriching any available OCR
dataset. Its modular structure allows to be modified to match individual user
requirements. The framework enables to comfortably scale the enlargement factor
of the available dataset. Furthermore, the proposed method is not restricted to
single frame OCR but can also be applied to video OCR. We demonstrate the
performance of our framework by augmenting a 15% subset of the common Brno
Mobile OCR dataset. Our proposed framework is capable of leveraging the
performance of OCR applications especially for small datasets. Applying the
proposed method, improvements of up to 2.79 percentage points in terms of
Character Error Rate (CER), and up to 7.88 percentage points in terms of Word
Error Rate (WER) are achieved on the subset. Especially the recognition of
challenging text lines can be improved. The CER may be decreased by up to 14.92
percentage points and the WER by up to 18.19 percentage points for this class.
Moreover, we are able to achieve smaller error rates when training on the 15%
subset augmented with the proposed method than on the original non-augmented
full dataset.
