Improving Accuracy and Explainability of Online Handwriting Recognition

TL;DR

This paper enhances online handwriting recognition accuracy using machine learning and deep learning models on the OnHW-chars dataset, while also emphasizing model explainability and providing reproducible results.

Contribution

It develops improved ML and DL models for handwriting recognition on the OnHW-chars dataset, achieving significant accuracy gains and adding explainability features.

Findings

ML models improve accuracy by 11.3%-23.56% over previous models

DL ensemble models improve accuracy by 3.08%-7.01%

Results are verifiable and reproducible via public repository

Abstract

Handwriting recognition technology allows recognizing a written text from a given data. The recognition task can target letters, symbols, or words, and the input data can be a digital image or recorded by various sensors. A wide range of applications from signature verification to electronic document processing can be realized by implementing efficient and accurate handwriting recognition algorithms. Over the years, there has been an increasing interest in experimenting with different types of technology to collect handwriting data, create datasets, and develop algorithms to recognize characters and symbols. More recently, the OnHW-chars dataset has been published that contains multivariate time series data of the English alphabet collected using a ballpoint pen fitted with sensors. The authors of OnHW-chars also provided some baseline results through their machine learning (ML) and deep learning (DL) classifiers. In this paper, we develop handwriting recognition models on the OnHW-chars dataset and improve the accuracy of previous models. More specifically, our ML models provide $11.3\%$-$23.56\%$ improvements over the previous ML models, and our optimized DL models with ensemble learning provide $3.08\%$-$7.01\%$ improvements over the previous DL models. In addition to our accuracy improvements over the spectrum, we aim to provide some level of explainability for our models to provide more logic behind chosen methods and why the models make sense for the data type in the dataset. Our results are verifiable and reproducible via the provided public repository.