This paper presents a linguistically-informed, non-machine-learning tool for classifying Written Cantonese, Standard Written Chinese, and the intermediate varieties used by Cantonese-speaking users from Hong Kong, which are often grouped into a single “Traditional Chinese” label. Our approach addresses the lack of textual materials for Cantonese NLP, a consequence of a lower sociolinguistic status of Written Cantonese and the interchangeable use of these varieties by users without sufficient language labeling. The tool utilizes key strings and quotation markers, which can be reduced to string operations, to effectively extract Written Cantonese sentences and documents from materials mixed with Standard Written Chinese. This allows for the flexible and efficient extraction of high-quality Cantonese data from large datasets, catering to specific classification needs. This implementation ensures that the tool can process large amounts of data at a low cost by bypassing model-inferencing, which is particularly significant for marginalized languages. The tool also aims to provide a baseline measure for future classification systems, and the approach may be applicable to other low-resource regional or diglossic languages.