  In this work, we study the problem of word-level confidence calibration for
scene-text recognition (STR). Although the topic of confidence calibration has
been an active research area for the last several decades, the case of
structured and sequence prediction calibration has been scarcely explored. We
analyze several recent STR methods and show that they are consistently
overconfident. We then focus on the calibration of STR models on the word
rather than the character level. In particular, we demonstrate that for
attention based decoders, calibration of individual character predictions
increases word-level calibration error compared to an uncalibrated model. In
addition, we apply existing calibration methodologies as well as new
sequence-based extensions to numerous STR models, demonstrating reduced
calibration error by up to a factor of nearly 7. Finally, we show consistently
improved accuracy results by applying our proposed sequence calibration method
as a preprocessing step to beam-search.
