# Improving Accuracy and Explainability of Online Handwriting Recognition

arXiv: https://arxiv.org/abs/2209.09102 · HTML: https://mlsanity.com/paper/2209.09102/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 15 of 15)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [Distributed and parallel time series feature extraction for industrial big data applications](https://mlsanity.com/paper/1610.07717/) (2016)
- [XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2009.04796/) (2020)
- [Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition](https://mlsanity.com/paper/1512.06747/) (2015)
- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture Classification](https://mlsanity.com/paper/1911.03803/) (2019)
- [MotherNets: Rapid Deep Ensemble Learning](https://mlsanity.com/paper/1809.04270/) (2018)
- [Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens](https://mlsanity.com/paper/2202.07036/) (2022)
- [High Accuracy Android Malware Detection Using Ensemble Learning](https://mlsanity.com/paper/1608.00835/) (2016)
- [On Meta-Learning for Dynamic Ensemble Selection](https://mlsanity.com/paper/1811.01743/) (2018)

## Built on by (most cited first, 2 of 2)

- [Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors](https://mlsanity.com/paper/2607.26708/) (2026)
- [Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network](https://mlsanity.com/paper/2607.26733/) (2026)

## Abstract (arXiv)

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.