TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with
  Pre-trained Language Model
Jiahao Lyu Jin Wei Gangyan Zeng Zeng Li Enze Xie Wei Wang Yu Zhou
Existing scene text spotters are designed to locate and transcribe texts from
images. However, it is challenging for a spotter to achieve precise detection
and recognition of scene texts simultaneously. Inspired by the glimpse-focus
spotting pipeline of human beings and impressive performances of Pre-trained
Language Models (PLMs) on visual tasks, we ask: 1) "Can machines spot texts
without precise detection just like human beings?", and if yes, 2) "Is text
block another alternative for scene text spotting other than word or
character?" To this end, our proposed scene text spotter leverages advanced
PLMs to enhance performance without fine-grained detection. Specifically, we
first use a simple detector for block-level text detection to obtain rough
positional information. Then, we finetune a PLM using a large-scale OCR dataset
to achieve accurate recognition. Benefiting from the comprehensive language
knowledge gained during the pre-training phase, the PLM-based recognition
module effectively handles complex scenarios, including multi-line, reversed,
occluded, and incomplete-detection texts. Taking advantage of the fine-tuned
language model on scene recognition benchmarks and the paradigm of text block
detection, extensive experiments demonstrate the superior performance of our
scene text spotter across multiple public benchmarks. Additionally, we attempt
to spot texts directly from an entire scene image to demonstrate the potential
of PLMs, even Large Language Models (LLMs).