Scene text recognition (STR) in the wild frequently encounters challenges
when coping with domain variations, font diversity, shape deformations, etc. A
straightforward solution is performing model fine-tuning tailored to a specific
scenario, but it is computationally intensive and requires multiple model
copies for various scenarios. Recent studies indicate that large language
models (LLMs) can learn from a few demonstration examples in a training-free
manner, termed "In-Context Learning" (ICL). Nevertheless, applying LLMs as a
text recognizer is unacceptably resource-consuming. Moreover, our pilot
experiments on LLMs show that ICL fails in STR, mainly attributed to the
insufficient incorporation of contextual information from diverse samples in
the training stage. To this end, we introduce E$^2$STR, a STR model trained
with context-rich scene text sequences, where the sequences are generated via
our proposed in-context training strategy. E$^2$STR demonstrates that a
regular-sized model is sufficient to achieve effective ICL capabilities in STR.
Extensive experiments show that E$^2$STR exhibits remarkable training-free
adaptation in various scenarios and outperforms even the fine-tuned
state-of-the-art approaches on public benchmarks.

Scene text recognition (STR) in the wild faces challenges due to domain variations, font diversity, shape deformations, etc. Recent studies show that large language models (LLMs) can learn from a few demonstration examples using In-Context Learning (ICL). However, applying LLMs as a text recognizer is resource-consuming. To address this, the paper introduces E$^2$STR, a STR model trained with context-rich scene text sequences, demonstrating effective ICL capabilities with a regular-sized model and outperforming fine-tuned approaches.