Although the optical character recognition (OCR) is a mature technology in theory and application, some obstacles still exist in studying scene text recognition (STR). The STR that the electricity nameplate text recognition belongs to always is limited by various factors such as the crappy quality of image, the instability of environments, and the differentiation of fonts. To correctly identify the information on the electricity nameplate, we propose a novel architecture which consists of two pipeline networks, an improved text recognition network based on Transformer OCR and an enhanced spelling error correction network based on Soft-Masked BERT. The former ensures that the glyph knowledge is left, and the latter is to preserve the expressions of diversity. To validate the effectiveness of our method, we evaluated it on a self-annotated electricity nameplate corpus and reported the across-the-board performance gains compared to competing prior models. We further discuss the ablation results for dissecting the gains obtained above.
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