Baidu Meizu Deep Learning Competition: Arithmetic Operation Recognition Using End-to-End Learning OCR Technologies

Abstract

The end-to-end learning approaches were proposed for an arithmetic expression recognition task in the Baidu Meizu Deep Learning Competition by a deep convolutional neural network (DCNN) with parallel dense layers and component-connection-based detection pipeline with the convolutional recurrent neural network (CRNN) model. Two effective pipelines for DCNN and CRNN to identify long and complex expressions are presented and compared. In the first task, a DCNN connected to parallel dense layers for digital arithmetic operations was developed, which achieves 99.985% accuracy. In the second task, the CRNN with connectionist temporal classification was adopted, combined with the text region detection technique to recognize more complex pictures with both assignment operations and calculation formulas, which achieves 98.087% accuracy.