Chinese Inertial GAN for Writing Signal Generation and Recognition

Yifeng Wang, Yi Zhao

Keywords: Inertial Sensors, Handwriting Recognition, Signal Generation, Human-Computer Interaction, Disabled People

Abstract:

Disabled people constitute a significant part of the global population, deserving of inclusive consideration and empathetic support. However, the current human-computer interaction based on keyboards may not meet the requirements of disabled people. The small size, ease of wearing, and low cost of inertial sensors make inertial sensor-based writing recognition a promising human-computer interaction option for disabled people. However, accurate recognition relies on massive inertial signal samples, which are hard to collect for the Chinese context due to the vast number of characters. Therefore, we design a Chinese inertial generative adversarial network (CI-GAN) containing Chinese glyph encoding (CGE), forced optimal transport (FOT), and semantic relevance alignment (SRA) to acquire unlimited high-quality training samples. Unlike existing vectorization focusing on the meaning of Chinese characters, CGE represents the shape and stroke features, providing glyph guidance for GAN to generate writing signals. FOT establishes a triple-consistency constraint between the input prompt, output signal features, and real signal features, ensuring the authenticity and semantic accuracy of the generated signals and preventing mode collapse and mixing. SRA constrains the consistency between the semantic relationships among multiple outputs and the corresponding input prompts, ensuring that similar inputs correspond to similar outputs (and vice versa), significantly alleviating the hallucination problem of generative models. The three modules guide the generator while also interacting with each other, forming a coupled system. By utilizing the massive training samples provided by CI-GAN, the performance of six widely used classifiers is improved from 6.7% to 98.4%, indicating that CI-GAN constructs a flexible and efficient data platform for Chinese inertial writing recognition. Furthermore, we release the first Chinese writing recognition dataset based on inertial sensors in GitHub.

Primary Area: datasets and benchmarks

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Submission Number: 6512