Dataset of inertial measurements for writing Punjabi characters using IMU sensors.

Abstract

This study introduces a comprehensive methodology for gathering datasets to recognize handwritten Punjabi alphabets, utilizing Inertial Measurement Units (IMUs) to capture the dynamic movement patterns inherent in handwriting. The approach considers the diverse writing styles found across Punjabi writers, which presents unique challenges due to regional variations in script. The dataset and collection system are designed to enhance recognition accuracy by harnessing this diversity. The data collection process involved recording handwriting movements from multiple participants, ensuring the dataset reflects a wide range of writing styles. By leveraging IMUs, the system tracks detailed handwriting motions, enhancing character recognition accuracy. The use of IMUs allows for the detailed tracking of handwriting movements, which is crucial for improving the accuracy of character recognition. Preliminary experimental results indicate that the dataset not only effectively captures the nuances of handwritten Punjabi but also demonstrates potential in recognizing handwritten English alphabets within the Indian context. This research contributes significantly to the field of pattern recognition, offering insights that could lead to the development of more robust handwriting recognition systems particularly suited for various linguistic and cultural settings.