MLFont: Machine Learning-Driven Font Subsetting and Range-Compact Unicode-Range Slicing for Low-Latency Web Typography

CSS3 @font-face rules with unicode-range descriptors partition web fonts into downloadable slices. While Latin fonts need ~20 KB in WOFF2, Pan-European (LGC) and East Asian (CJK) fonts contain 2,297-30,759 codepoints (158 KB-5.9 MB in WOFF2), where heuristic partitioning causes unused-glyph downloads, high HTTP RTT, CSS bloat, or missing-glyph fallbacks. We present MLFont, a machine learning and optimization framework that learns font slicing strategies from corpora via three stages: (1) a Zipfian High-Frequency Tier (most) isolating top-frequency codepoints; (2) a Latent Co-Occurrence Topic Tier using Online Variational Bayes LDA (Online LDA) or a Deep Neural Network Topic Model (DNNTM) with Shannon Entropy Difference Loss and Sinkhorn-Knopp normalization; and (3) a Memetic Range-Compact Genetic Algorithm (GA) with Potts-Viterbi dynamic programming and island bridging that minimizes topic discrepancy and CSS fragmentation. Across five Google Noto fonts (LGC, KR, JP, TC, SC), MLFont cuts 3G per-glyph latency by 56.7%-75.3% over single-file delivery and 46.1%-66.3% over equal-bin baselines on CJK with 100% coverage, reduces CSS intervals by 17.9%-41.7%, and achieves 1.56x-6.09x cold-cache speedups (65.4%-84.9% CJK and 44.7% LGC bandwidth savings) across 20 websites in simulator and Headless Chrome benchmarks (45/45 exact slice-set parity). Code: https://github.com/wq2012/mlfont Models: https://huggingface.co/collections/wq2012/mlfont