JSTR: Judgment Improves Scene Text Recognition
원문 초록
In this paper, we present a method for enhancing the accuracy of scene text recognition tasks by judging whether the image and text match each other. While previous studies focused on generating the recognition results from input images, our approach also considers the model's misrecognition results to understand its error tendencies, thus improving the text recognition pipeline. This method boosts text recognition accuracy by providing explicit feedback on the data that the model is likely to misrecognize by predicting correct or incorrect between the image and text. The experimental results on publicly available datasets demonstrate that our proposed method outperforms the baseline and state-of-the-art methods in scene text recognition.
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Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition
EventSTR: A Benchmark Dataset and Baselines for Event Stream based Scene Text Recognition
Ocean-OCR: Towards General OCR Application via a Vision-Language Model
TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition
SVTRv2: CTC Beats Encoder-Decoder Models in Scene Text Recognition
Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing
토론
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