  Scene text recognition has been an important, active research topic in
computer vision for years. Previous approaches mainly consider text as 1D
signals and cast scene text recognition as a sequence prediction problem, by
feat of CTC or attention based encoder-decoder framework, which is originally
designed for speech recognition. However, different from speech voices, which
are 1D signals, text instances are essentially distributed in 2D image spaces.
To adhere to and make use of the 2D nature of text for higher recognition
accuracy, we extend the vanilla CTC model to a second dimension, thus creating
2D-CTC. 2D-CTC can adaptively concentrate on most relevant features while
excluding the impact from clutters and noises in the background; It can also
naturally handle text instances with various forms (horizontal, oriented and
curved) while giving more interpretable intermediate predictions. The
experiments on standard benchmarks for scene text recognition, such as IIIT-5K,
ICDAR 2015, SVP-Perspective, and CUTE80, demonstrate that the proposed 2D-CTC
model outperforms state-of-the-art methods on the text of both regular and
irregular shapes. Moreover, 2D-CTC exhibits its superiority over prior art on
training and testing speed. Our implementation and models of 2D-CTC will be
made publicly available soon later.
