  Text spotting in natural scene images is of great importance for many image
understanding tasks. It includes two sub-tasks: text detection and recognition.
In this work, we propose a unified network that simultaneously localizes and
recognizes text with a single forward pass, avoiding intermediate processes
such as image cropping and feature re-calculation, word separation, and
character grouping.
  In contrast to existing approaches that consider text detection and
recognition as two distinct tasks and tackle them one by one, the proposed
framework settles these two tasks concurrently. The whole framework can be
trained end-to-end and is able to handle text of arbitrary shapes. The
convolutional features are calculated only once and shared by both detection
and recognition modules. Through multi-task training, the learned features
become more discriminate and improve the overall performance. By employing the
$2$D attention model in word recognition, the irregularity of text can be
robustly addressed. It provides the spatial location for each character, which
not only helps local feature extraction in word recognition, but also indicates
an orientation angle to refine text localization. Our proposed method has
achieved state-of-the-art performance on several standard text spotting
benchmarks, including both regular and irregular ones.
