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.
Abstract (translated)
本论文介绍了一种端到端的方法,以收集数据集,识别手写的英语单词,利用惯性测量单元(IMUs)并利用印度书写风格的多样性。IMUs用于捕捉与手写运动相关的动态运动模式,实现更精确的单词识别。由于印度地区的书写风格的多样性,引入了各种挑战。通过利用这种多样性,收集的数据集和收集系统旨在实现更高的识别精度。一些初步实验结果证明,该数据集在印度上下文中准确地识别手写的英语单词的有效性。这项研究可以扩展并贡献于模式识别领域,并提供有价值的洞察力,以开发改进的手写识别系统,尤其是在各种语言和文化背景下。
URL
https://arxiv.org/abs/2307.02480
