An intelligent recognition method for electrical work permits based on seed growth strategy and deep neural networks - LIAO Meiying; ZHOU Junhuang; ZHANG Yongjun (Zhejiang dianli)
In response to the digitalization needs of electrical work permits， an intelligent recognition method based on the seed growth strategy and dense convolutional network （DenseNet） is proposed. Firstly， during text detection， the seed growth strategy is employed to select an initial seed point and gradually expand the candidate areas， effectively improving the localization accuracy for irregular text areas. Then， during text recognition， the method combines DenseNet’s deep feature extraction capabilities with the CTC technique’s mechanism for aligning variable-length sequences， enhancing the recognition performance of character sequences. Finally， experiments demonstrate that the proposed method achieves higher accuracy in recognizing industry-specific characters， such as equipment codes and electrical symbols， significantly reducing misrecognition rates and effectively meeting the digital processing needs of electrical work permits. The method shows strong practical value and promising application potential.