  End-to-end text spotting aims to integrate scene text detection and
recognition into a unified framework. Dealing with the relationship between the
two sub-tasks plays a pivotal role in designing effective spotters. Although
Transformer-based methods eliminate the heuristic post-processing, they still
suffer from the synergy issue between the sub-tasks and low training
efficiency. Besides, they overlook the exploring on multilingual text spotting
which requires an extra script identification task. In this paper, we present
DeepSolo++, a simple DETR-like baseline that lets a single decoder with
explicit points solo for text detection, recognition, and script identification
simultaneously. Technically, for each text instance, we represent the character
sequence as ordered points and model them with learnable explicit point
queries. After passing a single decoder, the point queries have encoded
requisite text semantics and locations, thus can be further decoded to the
center line, boundary, script, and confidence of text via very simple
prediction heads in parallel. Furthermore, we show the surprisingly good
extensibility of our method, in terms of character class, language type, and
task. On the one hand, our method not only performs well in English scenes but
also masters the transcription with complex font structure and a thousand-level
character classes, such as Chinese. On the other hand, our DeepSolo++ achieves
better performance on the additionally introduced script identification task
with a simpler training pipeline compared with previous methods. In addition,
our models are also compatible with line annotations, which require much less
annotation cost than polygons. The code is available at
\url{https://github.com/ViTAE-Transformer/DeepSolo}.
