  A novel scene text recognizer based on Vision-Language Transformer (VLT) is
presented. Inspired by Levenshtein Transformer in the area of NLP, the proposed
method (named Levenshtein OCR, and LevOCR for short) explores an alternative
way for automatically transcribing textual content from cropped natural images.
Specifically, we cast the problem of scene text recognition as an iterative
sequence refinement process. The initial prediction sequence produced by a pure
vision model is encoded and fed into a cross-modal transformer to interact and
fuse with the visual features, to progressively approximate the ground truth.
The refinement process is accomplished via two basic character-level
operations: deletion and insertion, which are learned with imitation learning
and allow for parallel decoding, dynamic length change and good
interpretability. The quantitative experiments clearly demonstrate that LevOCR
achieves state-of-the-art performances on standard benchmarks and the
qualitative analyses verify the effectiveness and advantage of the proposed
LevOCR algorithm. Code is available at
https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/LevOCR.
