Reconstructed SqueezeNext with C-CBAM for offline handwritten Chinese character recognition.

ABSTRACT:

Background

Handwritten Chinese character recognition (HCCR) is a difficult problem in character recognition. Chinese characters are diverse and many of them are very similar. The HCCR model consumes a large number of computational resources during runtime, making it difficult to deploy to resource-limited development platforms.

Methods

In order to reduce the computational consumption and improve the operational efficiency of such models, an improved lightweight HCCR model is proposed in this article. We reconstructed the basic modules of the SqueezeNext network so that the model would be compatible with the introduced attention module and model compression techniques. The proposed Cross-stage Convolutional Block Attention Module (C-CBAM) redeploys the Spatial Attention Modul

SUBMITTER: Wu R 

PROVIDER: S-EPMC10496007 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature