Why India is completely ignoring Vision OCR for Roman Hinglish

I recently benchmarked several vision models on extracting handwritten Hinglish text in Roman script. Think everyday notes with items like "Hara Dhania", "Pahadi Aloo", or "Khurmani". The results highlight a massive gap in our AI ecosystem.

Here is what happens when you test the current options:

On-device tools like Google ML Kit completely fail. When fed handwritten Roman Hinglish, it spits out absolute gibberish and stray marks instead of usable text.

Western models perform better but still struggle heavily with local context. OpenAI models get the text structure right but aggressively overcorrect local words. For example, they will misread "Aaroo" (peach) as "Aloo" (potato) because they lack the local vocabulary context.

Gemini is worse in a different way because it suffers from confident hallucinations. It saw "Aaroo" and confidently extracted "Avocado". An honest approximate spelling is easy to manually fix, but a confident hallucination completely changes the meaning.

Open weights models like Llama 4 Scout handle the task a bit better by providing honest spelling mistakes instead of hallucinating. But they still struggle with exact Hinglish phrasing, misreading words like "Karela" as "Karla".

The real issue is that as of 2026, no Indian AI company offers a competitive vision API to solve this. Prominent startups like Sarvam AI and Krutrim are heavily focused on speech and text only. This leaves a huge blind spot for visual tasks involving local languages written in the English alphabet.

Global models are simply not optimized for our specific blend of languages and scripts, and domestic AI companies are completely ignoring the vision problem. We need local AI companies to step up and build multimodal models trained on native visual datasets.