A Dataset of Inertial Measurement Units for Handwritten English Alphabets

TL;DR

This paper introduces a new dataset of inertial measurement unit data capturing handwritten English alphabets in the Indian context, aiming to improve recognition accuracy across diverse writing styles.

Contribution

It presents an end-to-end methodology for collecting IMU-based handwriting datasets that account for regional diversity in Indian handwriting styles.

Findings

Preliminary results show high recognition accuracy using the dataset.

The dataset captures diverse handwriting styles across different Indian regions.

The methodology enhances pattern recognition in culturally diverse handwriting.

Abstract

This paper presents an end-to-end methodology for collecting datasets to recognize handwritten English alphabets by utilizing Inertial Measurement Units (IMUs) and leveraging the diversity present in the Indian writing style. The IMUs are utilized to capture the dynamic movement patterns associated with handwriting, enabling more accurate recognition of alphabets. The Indian context introduces various challenges due to the heterogeneity in writing styles across different regions and languages. By leveraging this diversity, the collected dataset and the collection system aim to achieve higher recognition accuracy. Some preliminary experimental results demonstrate the effectiveness of the dataset in accurately recognizing handwritten English alphabet in the Indian context. This research can be extended and contributes to the field of pattern recognition and offers valuable insights for developing improved systems for handwriting recognition, particularly in diverse linguistic and cultural contexts.