Document Classification Using File Names

Rapid document classification is critical in several time-sensitive applications like digital forensics and large-scale media classification. Traditional approaches that rely on heavy-duty deep learning models fall short due to high inference times over vast input datasets and computational resources associated with analyzing whole documents. In this paper, we present a method using lightweight supervised learning models, combined with a TF-IDF feature extraction-based tokenization method, to accurately and efficiently classify documents based solely on their file names, which substantially reduces inference time. Experiments on two datasets introduced in this paper show that our file name classifiers correctly predict more than 90% of in-scope documents with 99.63% and 96.57% accuracy while being 442x faster than more complex models such as DiT. Our results demonstrate that incorporating lightweight file name classification as a front-end to document analysis pipelines can efficiently process vast document datasets in critical scenarios, enabling fast and more reliable document classification.