UDGAM LABS · Transliterator · 0.88 MB, Hindi, runs in the browser

Lekhani

Lekhani turns Hindi typed in Latin letters into Devanagari: you type kaise ho, it writes कैसे हो. The whole thing is a 0.88 MB download (a 0.81M-parameter model at 4 bits, a list of 200,000 common Hindi words and a 10 KB JavaScript runner with no dependencies), and it runs in a web page on your own device, so nothing you type is sent anywhere. On the Aksharantar Hindi test it gets 65.9% of words exactly right, 7 points more than IndicXlit's model on its own (58.9%), which has 14 times as many parameters. IndicXlit's full Python package is still ahead (68.7%): it re-ranks with a 4.2-million-word list that takes 358 MB of memory.

Try it: on your device, in the browser · udgamlabs.com/models/lekhani

Quick start

Download the five files in this repository and serve them next to your page (any static host works).

<input id="in" placeholder="mera naam">
<p id="out"></p>
<script type="module">
  import { Transliterator } from "./translit.js";
  const tr = await Transliterator.load({
    model: "model.q4.gz", json: "model.json.gz", words: "words.bin.gz", rerank: "rerank.json",
  });
  const box = document.getElementById("in");
  box.addEventListener("input", () => {
    document.getElementById("out").textContent = tr.text(box.value);   // "kaise ho" → "कैसे हो"
  });
</script>

In Node 18 or newer:

import fs from "node:fs";
import { Transliterator } from "./translit.js";

const tr = await Transliterator.load({
  model: fs.readFileSync("model.q4.gz"), json: fs.readFileSync("model.json.gz"),
  words: fs.readFileSync("words.bin.gz"), rerank: JSON.parse(fs.readFileSync("rerank.json", "utf8")),
});
console.log(tr.text("aaj office mein meeting hai"));   // आज ऑफिस में मीटिंग है
console.log(tr.word("namaste").candidates.slice(0, 4).map((c) => c.text));

• tr.text(s) writes every run of Latin letters in Devanagari and keeps everything else (spaces, digits, punctuation, Devanagari) as it is.
• tr.word(w) returns best and candidates: the 8 spellings the model considered, best first, each with inList (whether it is one of the 200,000 listed words). Show the runners-up as suggestions if you build a keyboard.

Speed: about 2 ms a word with WebAssembly SIMD, which current browsers have (1.9 ms a word in Node on a Mac mini over all 9,991 Aksharantar test words; 2.4 to 2.8 ms on everyday words in Chrome and Firefox on an Intel laptop). Without SIMD it falls back to plain JavaScript, about 11 ms a word. The 1.0.0 runner took 18 ms a word.

Results

Aksharantar Hindi test (AI4Bharat): 9,991 romanised words, top-1 exact match against any reference spelling, scored word by word with one script on the same words for every system; 95% intervals from 2,000 bootstrap resamples, paired for gaps. Measured by us on 28 October 2026.

• Against IndicXlit's model on its own: +7.0 points (paired 95% interval +6.1 to +8.0).
• Against IndicXlit's full package: -2.8 points (-3.6 to -2.1). The package is more accurate if you can afford its word list.
• Hinglish chat set: 122 words people type in chat (everyday words, English words used in Hindi, brands, names), written by us and checked word by word by a native Hindi speaker. Lekhani gets 113, one more than IndicXlit's package (112). The set is too small to call a few words' difference.
• Other open transliterators we ran on the same test, word by word, scored far lower: IB-Emper hindi-translit-v3 (15M parameters, its model on its own) 48.6%, UnimeType Transliteration 4B 24.6% (v1.0) and 20.0% (v2.0, released 28 Oct 2026, on a fixed 2,000-word sample), indic-xlit-270M 21.7%, indic-xlit-50M 14.8%, romanized-hindi-transliterator-60M 9.1%, UnimeType 2.0 0.8B 8.0%, rule-based typing schemes 5-12%. UnimeType's models are built for whole sentences, so their numbers are word-level results only.
• The browser runner gives the same answer and the same 8 candidates as our Python reference on all 9,991 test words.

Where it falls short

• About one word in three on this test is wrong at top-1. The test is hard: many rare words and names. On everyday chat words it does much better (42 of 44 in our chat set), but it still makes mistakes there.
• Some common words come out wrong: kal → काल (कल is its second choice), delhi → देल्ही (should be दिल्ली).
• No sentence context. It writes one word at a time, so main ("I") and mein ("in") both become में: main ghar ja raha hoon → में घर जा रहा हूं (should be मैं).
• IndicXlit's full package is 2.8 points more accurate on the main test.
• Hindi only, Latin to Devanagari only.
• Speed was measured on one Mac mini. Phones and older laptops are not measured yet.

How it was trained

A small encoder-decoder transformer (811,904 parameters), trained from random weights on about 4.3 million pairs of romanised and Devanagari Hindi words, and stored at 4 bits. The pairs come from Aksharantar (its CC0 and CC-BY parts; not its share-alike part) and from a teacher: IndicXlit wrote the many ways people type common Hindi words from IndicCorp v2, and the Hindi spellings of English words. At run time the model proposes 8 spellings and the 200,000-word list, built from IndicCorp v2 word counts, moves a common real word up when the model is unsure. Words that are not in the list still work from the model alone. No test word was in the training pairs.

Intended use and limitations

For typing Hindi in Latin letters on websites, apps and tools: search boxes, forms, chat, notes, a keyboard's suggestions. It works one word at a time and does not translate. Show users the runners-up so they can fix a wrong word, and keep their own spelling if they do. It is not meant for names on legal or official documents without a human check.

Changes

• 1.2.0 (4 Nov 2026): common words cut short are fixed. When the best spelling ends in a bare virama (्), which a Hindi word almost never does, the runner now takes the best candidate without one, also trying a few spelling variants of the input (for example acha → accha) and keeping only listed words. acha and achha now give अच्छा. It changes about 0.1% of words: the Aksharantar Hindi test is unchanged at 65.9% (one word fixed, one broken), and the Hinglish chat set rises from 112 to 113. Same model and word list; speed is unchanged.
• 1.1.0 (2 Nov 2026): the runner is about 10 times faster (batched beam search, WebAssembly SIMD kernels with a plain-JavaScript fallback, a GELU table, leaner beam search and a 2,000-word cache), contributed by Manit Roy (GitHub #1). Same model and word list: the same best word and the same 8 candidates, in the same order, on all 9,991 Aksharantar Hindi test words. The download is 0.88 MB (was 0.87 MB).
• 1.0.0 (30 Oct 2026): first release.

Licence

Model weights and translit.js: Apache-2.0. The word list (words.bin.gz): CC0. See NOTICE for the credits: Aksharantar (AI4Bharat; CC-BY hand-collected part, CC0 mined parts), IndicXlit (AI4Bharat, MIT) as the teacher, IndicCorp v2 (AI4Bharat, CC0) for the word counts, and the CMU Pronouncing Dictionary (BSD-style) for English words.

Citation

@misc{udgamlabs2026lekhani,
  title  = {Lekhani: a 0.87 MB in-browser Hindi transliterator},
  author = {Prasad, Jitesh K},
  year   = {2026},
  howpublished = {Udgam Labs},
  url    = {https://huggingface.co/udgamlabs/Lekhani}
}

Please also cite Aksharantar (Madhani et al., 2022, arXiv:2205.03018), whose data and IndicXlit model made this possible.

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About Udgam Labs. Udgam Labs is an independent AI research lab. We work from first principles: before we build, we understand why things work, whether we are training a model, fine-tuning one or applying AI to a new problem. Udgam (उद्गम) is Sanskrit for origin. udgamlabs.com