Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network
This addresses the problem of accurately recognizing handwritten mathematical expressions for applications in education and document digitization, representing an incremental improvement over existing methods.
The paper tackled offline handwritten mathematical expression recognition by proposing a neural network model combining Multi-Scale CNN and Attention RNN to convert 2D expressions into 1D LaTeX sequences, achieving a WER error of 25.715% and ExpRate of 28.216%.
Offline handwritten mathematical expression recognition is a challenging task, because handwritten mathematical expressions mainly have two problems in the process of recognition. On one hand, it is how to correctly recognize different mathematical symbols. On the other hand, it is how to correctly recognize the two-dimensional structure existing in mathematical expressions. Inspired by recent work in deep learning, a new neural network model that combines a Multi-Scale convolutional neural network (CNN) with an Attention recurrent neural network (RNN) is proposed to identify two-dimensional handwritten mathematical expressions as one-dimensional LaTeX sequences. As a result, the model proposed in the present work has achieved a WER error of 25.715% and ExpRate of 28.216%.