Domain Adaption in Sequence-to-Sequence Scene Text Recognition

Zheng Li, Joshua Smith, Sujoy Chakraborty
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Abstract

Domain adaption techniques such as gradually vanishing bridge (GVB), have shown promising results in image classification problems. However, their efficacy in sequence-tosequence scene text recognition (STR) is yet to be known. In this paper, we combine GVB and connectionist temporal classification (CTC) techniques in STR model to improve the text recognition performance. The proposed approach is evaluated on publicly available datasets. Experimental results show the performance gain compared with state-of-the-art approaches.
序列到序列场景文本识别中的领域自适应
逐渐消失桥(GVB)等领域自适应技术在图像分类问题中显示出良好的效果。然而,它们在序列到序列的场景文本识别(STR)中的有效性尚不清楚。本文在STR模型中结合GVB和连接时态分类(CTC)技术来提高文本识别性能。所提出的方法在公开可用的数据集上进行了评估。实验结果表明,与现有方法相比,该方法的性能有所提高。
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