# Toward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling

## TL;DR

This paper introduces a CNN-based approach for online handwritten Chinese character recognition, utilizing DropDistortion, path signature features, and spatial stochastic max-pooling to achieve state-of-the-art accuracy.

## Contribution

It proposes a novel training strategy called DropDistortion and combines it with path signature features and spatial stochastic max-pooling for improved recognition accuracy.

## Key findings

- Achieved state-of-the-art accuracy on three datasets.
- DropDistortion enhances model generalization.
- Spatial stochastic max-pooling improves feature robustness.

## Abstract

This paper presents an investigation of several techniques that increase the accuracy of online handwritten Chinese character recognition (HCCR). We propose a new training strategy named DropDistortion to train a deep convolutional neural network (DCNN) with distorted samples. DropDistortion gradually lowers the degree of character distortion during training, which allows the DCNN to better generalize. Path signature is used to extract effective features for online characters. Further improvement is achieved by employing spatial stochastic max-pooling as a method of feature map distortion and model averaging. Experiments were carried out on three publicly available datasets, namely CASIA-OLHWDB 1.0, CASIA-OLHWDB 1.1, and the ICDAR2013 online HCCR competition dataset. The proposed techniques yield state-of-the-art recognition accuracies of 97.67%, 97.30%, and 97.99%, respectively.