Hylog: A Hybrid Approach to Logging Text Production in Non-alphabetic Scripts

2026-01-25

AI Summary

Traditional keyloggers struggle to capture the screen conversion processes of Input Method Editors (IMEs) for non-alphabetic scripts, creating a methodological gap in cognitive research on text generation. This study proposes a hybrid logging system that leverages modular open-source plugins to synchronously record keystroke events and rendered text within Microsoft Word and Google Chrome. For the first time, this approach enables fine-grained, dual-track synchronization of Latin character input, Chinese character candidate selection, and confirmation actions during IME-based typing. Evaluated in a Simplified Chinese translation task, the system successfully captures input details inaccessible to conventional tools, demonstrating its technical feasibility and offering a novel methodological and data foundation for investigating cross-linguistic cognitive mechanisms underlying text production.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Large-scale security measurements

Abstract

Research keyloggers are essential for cognitive studies of text production, yet most fail to capture the on-screen transformations performed by Input Method Editors (IMEs) for non-alphabetic scripts. To address this methodological gap, we present Hylog, a novel hybrid logging system that combines analytical keylogging with ecological text logging for a more complete and finer-grained analysis. Our modular, open-source system uses plug-ins for standard applications (Microsoft Word, Google Chrome) to capture both keyboard output and rendered text, which a hybridizer module then synchronizes into a dual trace. To validate the system's technical feasibility and demonstrate its analytical capabilities, we conducted a proof-of-concept study where two volunteers translated a text into simplified Chinese. Hylog successfully captured keypresses and temporal intervals between Latin letters, Chinese characters, and IME confirmations -- some measurements invisible to traditional keyloggers. The resulting data enable the formulation of new, testable hypotheses about the cognitive restrictions and affordances at different linguistic layers in IME-mediated typing. Our plug-in architecture enables extension to other IME systems and fosters more inclusive multilingual text-production research.

Problem

• keylogging
• Input Method Editor
• non-alphabetic scripts
• text production
• cognitive studies

Innovation

• hybrid logging
• input method editor (IME)
• non-alphabetic scripts
• keylogging
• text production