Attention-based handwritten Chinese recognition for power grid maintenance documents

Recognizing handwritten Chinese documents can improve efficiency and productivity, which makes it a crucial task for power grid enterprises. This paper proposes a novel handwritten document recognition method to enhance recognition accuracy. First, spatial features are extracted from the input images using an inception module, which captures multi-scale spatial characteristics. Subsequently, a space channel parallel attention module is employed to emphasize significant features and suppress interference. The spatial features are then transformed by a bidirectional long short-term memory network, which predicts the probabilities of outputting Chinese characters. Finally, a transcription layer computes the prediction loss for each character, and the final prediction results are obtained after removing redundant placeholders. Validation experiments demonstrate that the accurate rate and correct rate of the proposed method reach 96.92% and 97.66%, respectively, indicating its effectiveness in capturing handwritten character features and improving accuracy, even under the challenge of diverse handwriting styles.