mlfont-notosansjp: Range-Compact MLFont Slicing for Noto Sans JP (Japanese)

Overview

Pretrained MLFont machine-learning-guided font slicing model and WOFF2 slice bundle for Noto Sans JP (Japanese) (NotoSansJP-Regular.otf, 16,663 Unicode codepoints partitioned into 62 range-compact slices).

• Paper: MLFont: Machine Learning-Driven Font Subsetting and Range-Compact Unicode-Range Slicing for Low-Latency Web Typography (PDF) — Quan Wang and Yiran Mao (arXiv:2610.24937)
• Background & References: Open-source reproduction of Google's machine learning font slicing technology (Official Post, Defensive Publication)
• Open-Source Repository: https://github.com/wq2012/mlfont
• PyPI Package: https://pypi.org/project/mlfont/

Using This Sliced Font in Your Website (Frontend Developer Guide)

While basic Latin webfonts are typically 20–100 KB, Pan-Unicode and East Asian fonts like Noto Sans JP (Japanese) (NotoSansJP-Regular.otf) are much larger (4.67 MB / 4779.7 KB (4,894,460 bytes) in WOFF2) because they include 16,663 characters. In any large corpus of documents, there are strong co-occurrence patterns in character usage. By scanning webpages in ja, MLFont models which characters are most likely to appear together, informing a static subset slicing strategy that sorts all 16,663 glyphs into 62 slices (7,298 compact CSS unicode-range intervals).

Using this method, your entire web page renders in Noto Sans JP (Japanese) while the browser only downloads the small subset of .woff2 font slices required for the characters present on that page—which is dramatically faster than loading the whole font. Cross-page and cross-site browser caching further increases the latency benefits over time. (Note: The slicing optimization works automatically in all modern browsers that support CSS unicode-range.)

1. Link to Sliced Font CSS (HTML <link> or CSS @import)

Add a <link> element to the <head> section of your HTML:

<link rel="stylesheet"
      href="https://cdn.jsdelivr.net/gh/wq2012/mlfont@main/pretrained_models/mlfont-notosansjp/font_sliced_cdn.css">

Or in your CSS / SCSS stylesheet:

@import url('https://cdn.jsdelivr.net/gh/wq2012/mlfont@main/pretrained_models/mlfont-notosansjp/font_sliced_cdn.css');

2. Designate Font Family in CSS

Style your text with the 'Noto Sans JP Sliced' family in your CSS:

body {
  font-family: 'Noto Sans JP Sliced', 'NotoSansJP-Regular Sliced', Roboto, 'Noto Sans', sans-serif;
}

(Note: font_sliced_cdn.css streams the .woff2 slices on-demand directly from Hugging Face's global CDN https://huggingface.co/wq2012/mlfont-notosansjp/resolve/main/woff2/... with font-display: swap and CORS Access-Control-Allow-Origin: *, so you do not need to host the .woff2 files yourself. To self-host on your own server/CDN, download this repository and link font_sliced.css alongside the woff2/ folder.)

3. Alternative: One-Line Drop-In JavaScript Loader (mlfont.js)

You can also load the sliced stylesheet directly from Hugging Face CDN using a single <script> tag with data-mlfont:

<script src="https://cdn.jsdelivr.net/gh/wq2012/mlfont@main/web/mlfont.js"
        data-mlfont="notosansjp"
        async></script>

Or programmatically in JavaScript:

MLFont.load('notosansjp');

4. Latency Metrics & Browser Telemetry (Recommended — mlfont.js)

Included in this bundle (mlfont.js) is a small, zero-dependency JS library that accesses the HTML5 Performance API to measure webfont slice performance. No user-specific data is collected. The JS library collects:

1. Download time metrics of individual font slices (slice index, TTFB, download duration, transfer bytes, cache hit status)
2. Download time of the sliced @font-face CSS file
3. Page onload and First Contentful Paint (FCP) time

To include the library, add the following code to the bottom of your <head> section:

<script src="https://cdn.jsdelivr.net/gh/wq2012/mlfont@main/web/mlfont.js" async></script>

You can inspect downloaded slice metrics in your browser DevTools console via MLFont.printReport() or MLFont.getMetrics(), or forward anonymized telemetry beacons to your own analytics endpoint via data-endpoint="https://your-analytics.example.com/mlfont-timing".

5. Simulating Real-Website Font Loading Latency & Headless Chrome Verification

Across 3 real-world sample websites for Noto Sans JP (Japanese) (full unsliced WOFF2: 3,260.2 KB), range_compact_most500_500_topics60 downloads an average of only 10.33 / 62 slices (1,062.9 KB vs. 3,279.1 KB unsliced, saving 67.6% bandwidth). On Cable-5Mbps (5 Mbps, 28 ms RTT), this achieves a 2.94x cold-cache load speedup (1,851 ms vs. 5,437 ms for single_slice), with First Text Paint at 689 ms and Warm-Session Load at 568 ms (and 2.94x speedup on GIN-3g: 23,117 ms vs. 67,965 ms):

Real Headless Chrome (v155.0.8059.79) Verification (Cable-5Mbps)

Verified against physical .woff2 files in Headless Chrome (--headless=new --dump-dom) via the W3C document.fonts (FontFaceSet) API (9/9 = 100.0% exact slice-set match between Chromium Blink's unicode-range engine and WebsiteFontSimulator).

You can test your own HTML files, live URLs, or text snippets against mlfont-notosansjp before deploying using the mlfont-simulate CLI, the Python WebsiteFontSimulator API, or the interactive side-by-side Website Font Loading Simulator in web/demo.html:

# Simulate a local HTML file with mlfont-notosansjp across all 7 network profiles:
mlfont-simulate --font_key notosansjp --html_file path/to/index.html --network_profile all

# Simulate a live URL and verify in real Headless Chrome:
mlfont-simulate --font_key notosansjp --url https://example.com --network_profile Cable-5Mbps --headless_chrome

Training Configuration

• Target Font: NotoSansJP-Regular.otf (16,663 Unicode codepoints)
• Training & Evaluation Corpus: wikimedia/wikipedia (20231101, languages: ja), 1,600 training articles and 400 uniformly sampled testing articles
• Slicing Architecture: Tiered Document-Frequency Slicing + Online Variational LDA & Deep Neural Network Topic Model (DNNTM) + Range-Compact Genetic Algorithm (mlfont preset: japanese)
• Total Slices: 62 slices (7,298 total CSS unicode-range intervals)
• Physical WOFF2 Bundle Size: 4.67 MB / 4779.7 KB (4,894,460 bytes) across 62 .woff2 files (companion CSS: 85.7 KB). Note: physical .woff2 slicing replicates shared OpenType tables (cmap, hmtx, loca, GSUB, GPOS) into each standalone subset file (increasing static CDN disk footprint by +46.6% to +175.3% vs. a monolithic WOFF2), whereas individual webpages only fetch the small subset of triggered slices (saving 65.4%–84.9% per-page bandwidth on CJK and 44.7% on LGC).
• Training Wall-Time: 46.0 seconds on CPU/GPU

Model Files Included

• font_sliced_cdn.css: Zero-hosting CDN @font-face stylesheet (font-family: 'Noto Sans JP Sliced') streaming .woff2 slices directly from Hugging Face CDN
• font_sliced.css: Self-hosted @font-face stylesheet (font-family: 'NotoSansJP-Regular Sliced', url(woff2/NotoSansJP-Regular_slice_XXX.woff2)) with compact unicode-range descriptors
• mlfont.js: Browser JS loader (MLFont.load) & HTML5 Performance API slice latency telemetry library (MLFont.getMetrics / MLFont.printReport)
• woff2/NotoSansJP-Regular_slice_XXX.woff2: 62 physical WOFF2 font slices generated via fontTools.subset (plus woff2/NotoSansJP-Regular_sliced.css)
• nam/slice_XXX.nam: 62 .nam codepoint subset files
• model.safetensors: Learned topic-codepoint weight matrices (lambda_matrix, dnntm_lambda_matrix), codepoints, remaining_codepoints, frequency_array, and dna_assignment in SafeTensors format
• strategy.json: Range-Compact GA SlicingStrategy definition and hyperparameters
• olda_strategy.json: Online LDA + Greedy SlicingStrategy definition
• dnntm_strategy.json: DNNTM (Entropy Difference Loss) + Greedy SlicingStrategy definition
• test_samples.json: Exact held-out evaluation codepoint sets (400 test pages)
• config.json: Model and font bundle summary metadata (including per-slice WOFF2 byte sizes)
• evaluation_metrics.json: Full benchmark metrics across all 7 network/session profiles

Benchmark Performance (400 Wikipedia Test Pages, Session Length L = 10)

Python API Usage (ML Researchers & Custom Font Slicing)

Install mlfont:

pip install mlfont huggingface_hub

Load the pretrained bundle (Range-Compact GA, Online LDA, and DNNTM) in Python:

import mlfont

bundle = mlfont.load_pretrained("wq2012/mlfont-notosansjp")
print("GA:", bundle.strategy.name, "ranges:", bundle.strategy.count_unicode_ranges())
print("LDA:", bundle.olda_strategy.name, "ranges:", bundle.olda_strategy.count_unicode_ranges())
print("DNNTM:", bundle.dnntm_strategy.name, "ranges:", bundle.dnntm_strategy.count_unicode_ranges())

Citation

@article{wang2026mlfont,
  title={MLFont: Machine Learning-Driven Font Subsetting and Range-Compact Unicode-Range Slicing for Low-Latency Web Typography},
  author={Wang, Quan and Mao, Yiran},
  journal={arXiv preprint arXiv:2610.24937},
  eprint={2610.24937},
  archivePrefix={arXiv},
  year={2026},
  url={https://arxiv.org/abs/2610.24937}
}

@article{wang2017learning,
  title={Learning Better Font Slicing Strategies from Data},
  author={Wang, Quan and Mao, Yiran},
  journal={Technical Disclosure Commons, Defensive Publications Series},
  volume={906},
  year={2017},
  url={https://www.tdcommons.org/dpubs_series/906/}
}