  Many new proposals for scene text recognition (STR) models have been
introduced in recent years. While each claim to have pushed the boundary of the
technology, a holistic and fair comparison has been largely missing in the
field due to the inconsistent choices of training and evaluation datasets. This
paper addresses this difficulty with three major contributions. First, we
examine the inconsistencies of training and evaluation datasets, and the
performance gap results from inconsistencies. Second, we introduce a unified
four-stage STR framework that most existing STR models fit into. Using this
framework allows for the extensive evaluation of previously proposed STR
modules and the discovery of previously unexplored module combinations. Third,
we analyze the module-wise contributions to performance in terms of accuracy,
speed, and memory demand, under one consistent set of training and evaluation
datasets. Such analyses clean up the hindrance on the current comparisons to
understand the performance gain of the existing modules.
