Yi Characters Online Handwriting Recognition Models Based on Recurrent Neural Network: RnnNet-Yi and ParallelRnnNet-Yi

Abstract

As the sixth largest minority language in China, the Yi language is used by 8 million people and records the development of human civilization. Deep learning has been widely used and effective in mainstream characters’ recognition, but there are few achievements in recognition of Yi characters, particularly online handwriting recognition. Most of the Yi strokes are curved, and the writing is irregular. Consequently, there are some problems, such as arbitrarily change for the writing order, multiple strokes concatenated or abbreviated, stroke position offset, and so on. Due to different sample collection equipment, there is also dimensional diversity and sampling frequency diversity between sample collection devices, which will bring more significant interference to the identification. In this paper, we construct a Yi online handwriting recognition database and propose two Yi online handwriting recognition models based on different usage scenarios: RnnNet-Yi (for high accuracy requirements) and ParallelRnnNet-Yi (for resource-constrained lightweight requirements), merging deep learning and feature extraction methods. The experimental results verify the effectiveness of the models proposed in this paper in upgrading the accuracy and training speed of Yi online handwriting recognition, which fills the gap in Yi online handwriting recognition research.