Word recognition is a basic task for intelligent K-12 education, which leads to further complex tasks including grammar checking, composition grading, etc. However, there is little study about recognition of students’ handwritten words. We propose a novel convolutional recurrent neural network architecture that combines attention mechanism with connectionist time classification loss for student handwritten words. And the method also performs excellently in handwritings of adults. With an ablation study, we show that our method is better than its counterpart without attention. The CRNN with attention model we proposed achieve superior performance on word recognition and has the potential to support applications of intelligent K-12 education.
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