  Transcription-only Supervised Text Spotting aims to learn text spotters
relying only on transcriptions but no text boundaries for supervision, thus
eliminating expensive boundary annotation. The crux of this task lies in
locating each transcription in scene text images without location annotations.
In this work, we formulate this challenging problem as a Weakly Supervised
Cross-modality Contrastive Learning problem, and design a simple yet effective
model dubbed WeCromCL that is able to detect each transcription in a scene
image in a weakly supervised manner. Unlike typical methods for cross-modality
contrastive learning that focus on modeling the holistic semantic correlation
between an entire image and a text description, our WeCromCL conducts atomistic
contrastive learning to model the character-wise appearance consistency between
a text transcription and its correlated region in a scene image to detect an
anchor point for the transcription in a weakly supervised manner. The detected
anchor points by WeCromCL are further used as pseudo location labels to guide
the learning of text spotting. Extensive experiments on four challenging
benchmarks demonstrate the superior performance of our model over other
methods. Code will be released.
