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.

翻译：本文提出了一种端到端的数据集收集方法，通过利用惯性测量单元（IMU）并结合印度书写风格的多样性，实现对英文字母手写体的识别。IMU用于捕捉与手写相关的动态运动模式，从而能够更准确地识别字母。印度环境因不同地区和语言间书写风格的异质性而引入了多种挑战。通过利用这种多样性，所收集的数据集及收集系统旨在实现更高的识别准确率。初步实验结果表明，该数据集在印度环境下能够有效识别手写英文字母。本研究可进一步拓展，为模式识别领域做出贡献，并为开发更优化的手写识别系统（尤其是在多样化的语言和文化背景下）提供宝贵见解。