VL-Reader: Vision and Language Reconstructor is an Effective Scene Text Recognizer
<|reference_start|>VL-Reader: Vision and Language Reconstructor is an Effective Scene Text Recognizer: Text recognition is an inherent integration of vision and language, encompassing the visual texture in stroke patterns and the semantic context among the character sequences. Towards advanced text recognition, there are three key challenges: (1) an encoder capable of representing the visual and semantic distributions; (2) a decoder that ensures the alignment between vision and semantics; and (3) consistency in the framework during pre-training, if it exists, and fine-tuning. Inspired by masked autoencoding, a successful pre-training strategy in both vision and language, we propose an innovative scene text recognition approach, named VL-Reader. The novelty of the VL-Reader lies in the pervasive interplay between vision and language throughout the entire process. Concretely, we first introduce a Masked Visual-Linguistic Reconstruction (MVLR) objective, which aims at simultaneously modeling visual and linguistic information. Then, we design a Masked Visual-Linguistic Decoder (MVLD) to further leverage masked vision-language context and achieve bi-modal feature interaction. The architecture of VL-Reader maintains consistency from pre-training to fine-tuning. In the pre-training stage, VL-Reader reconstructs both masked visual and text tokens, while in the fine-tuning stage, the network degrades to reconstruct all characters from an image without any masked regions. VL-reader achieves an average accuracy of 97.1% on six typical datasets, surpassing the SOTA by 1.1%. The improvement was even more significant on challenging datasets. The results demonstrate that vision and language reconstructor can serve as an effective scene text recognizer.<|reference_end|>
@article{zhong2024vl-reader:,
    title={VL-Reader: Vision and Language Reconstructor is an Effective Scene Text
  Recognizer},
    author={Humen Zhong, Zhibo Yang, Zhaohai Li, Peng Wang, Jun Tang, Wenqing
  Cheng, Cong Yao},
    journal={arXiv preprint arXiv:2409.11656},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11656},
    primaryClass={cs.CV}
}