Abstract
Recognizing irregular text in real industrial scenes is a challenging task due to the background clutter, low resolutions or distortions. In this work, an attention-based text detection and recognition method for terminals of current transformer’s secondary circuit is proposed. It consists of three major components: pre-processing, text detection and text recognition. In text recognition module, a novel spatial temporal embedding is designed to better utilize the positional information. During training, the proposed framework only requires sequence-level annotations, instead of extra fine-grained character-level boxes or segmentation masks as in previous work. Despite its simplicity, the proposed method achieves good performance on the dataset collected in actual working scene.
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