  In the deployment of scene-text spotting systems on mobile platforms,
lightweight models with low computation are preferable. In concept, end-to-end
(E2E) text spotting is suitable for such purposes because it performs text
detection and recognition in a single model. However, current state-of-the-art
E2E methods rely on heavy feature extractors, recurrent sequence modellings,
and complex shape aligners to pursue accuracy, which means their computations
are still heavy. We explore the opposite direction: How far can we go without
bells and whistles in E2E text spotting? To this end, we propose a
text-spotting method that consists of simple convolutions and a few
post-processes, named Context-Free TextSpotter. Experiments using standard
benchmarks show that Context-Free TextSpotter achieves real-time text spotting
on a GPU with only three million parameters, which is the smallest and fastest
among existing deep text spotters, with an acceptable transcription quality
degradation compared to heavier ones. Further, we demonstrate that our text
spotter can run on a smartphone with affordable latency, which is valuable for
building stand-alone OCR applications.
