Computer Science > Computer Vision and Pattern Recognition
[Submitted on 28 Feb 2023 (v1), last revised 26 Mar 2023 (this version, v3)]
Title:Turning a CLIP Model into a Scene Text Detector
View PDFAbstract:The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language knowledge. Scene text, which contains rich textual and visual information, has an inherent connection with a model like CLIP. Recently, pretraining approaches based on vision language models have made effective progresses in the field of text detection. In contrast to these works, this paper proposes a new method, termed TCM, focusing on Turning the CLIP Model directly for text detection without pretraining process. We demonstrate the advantages of the proposed TCM as follows: (1) The underlying principle of our framework can be applied to improve existing scene text detector. (2) It facilitates the few-shot training capability of existing methods, e.g., by using 10% of labeled data, we significantly improve the performance of the baseline method with an average of 22% in terms of the F-measure on 4 benchmarks. (3) By turning the CLIP model into existing scene text detection methods, we further achieve promising domain adaptation ability. The code will be publicly released at this https URL.
Submission history
From: Wenwen Yu [view email][v1] Tue, 28 Feb 2023 06:06:12 UTC (1,963 KB)
[v2] Wed, 1 Mar 2023 04:36:26 UTC (1,964 KB)
[v3] Sun, 26 Mar 2023 12:58:19 UTC (1,964 KB)
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