Romanized Hindi has no standard spelling: the same word may be typed nahi, nahin, or nai, and no formal romanization rule or general-purpose transliteration system captures this typed convention well. Transliteration quality therefore depends as much on which spelling a user would actually type as on phonological plausibility. We present LIPI, a compact, encoder-only Devanagari-to-Roman transliteration model (~0.5M parameters) that represents each akshara compositionally from its ordered Unicode codepoints, expands it into six learned CTC emission slots, and encodes the frames with a two-layer bidirectional GRU under a connectionist-temporal-classification objective. The model is trained on a usage-ranked lexicon we build only from the training partitions of Dakshina, Xlit-Crowd, and Aksharantar, ranked by an attestation-gated social-usage prior so that a frequent Roman spelling is preferred only when it is also attested as a transliteration of the source word. LIPI is on par with the much larger multilingual IndicXlit baseline on general word transliteration (Aksharantar) and far stronger on naturally typed queries (FIRE 2013: 73.2 vs. 34.8 exact match), roughly doubling both baselines’ word match on Dakshina and IndoNLP sentences while remaining small enough for on-device use. The word-level gains are statistically significant and hold on words unseen in training, and an emission-slot ablation isolates the learned expansion’s contribution. The model, lexicon, and full benchmark results are publicly released at https://github.com/ABHISHEKgauti25/lipi.