  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.
