Abstract:
In this paper we propose a novel end-to-end framework for mathematical expression (ME) recognition. The method uses a convolutional neural network (CNN) to perform mathem...Show MoreMetadata
Abstract:
In this paper we propose a novel end-to-end framework for mathematical expression (ME) recognition. The method uses a convolutional neural network (CNN) to perform mathematical symbol detection and recognition simultaneously incorporating spatial context, and can handle multi-part and touching symbols effectively. To evaluate the performance, we provide a benchmark that contains MEs both from real-life and synthetic data. Images in our dataset undergo multiple variations such as viewpoint, illumination and background. For training, we use pure synthetic data for saving human labeling effort. The proposed method achieved 87% accuracy of total correct for clear images and 45% for cluttered ones.
Date of Conference: 04-08 December 2016
Date Added to IEEE Xplore: 24 April 2017
ISBN Information: