Vernacular NLP: Engineering Hindi-English Code-Mixed Speech & Public Gazette Parsing
What it takes to turn Indian public employment gazettes, Hindi PDF tables and code-mixed announcements into fields a job seeker can trust.
Dhruw Kumar Singh
Infrastructure & Reliability Lead
The Reality of Regional Indian Document Parsing
Public employment notifications (Sarkari Gazettes) in North India present unique document challenges: scanned physical printouts with ink smudges, complex multi-script Hindi/English (Hinglish) code-mixing, and critical eligibility tables embedded in unstructured PDF layouts.
Standard international OCR models degrade badly on Devanagari script conjuncts (युग्माक्षर) and misread eligibility age criteria, which is precisely how an eligible candidate misses a deadline.
Bilingual Entity Normalization Engine
A useful pipeline extracts four data points from every employment notification: Eligibility Qualifications, Age Limits with category relaxations, Important Application Deadlines, and Official Direct Submission Links.
Pair a Devanagari-capable OCR pass with regex boundary detectors so each of those four fields lands in a fixed shape. The regex detectors are cheap and easy to test, which is the point: check every extraction against the source gazette and show the reader a low-confidence flag, rather than publishing an accuracy percentage nobody can reproduce.
export interface GazetteJobDigest {
notificationId: string;
departmentName: string; // e.g., "UPSSSC / UP Police"
postTitleHindi: string;
postTitleEnglish: string;
totalVacancies: number;
eligibility: {
minEducation: string;
ageMin: number;
ageMax: number;
categoryRelaxations: Record<string, number>;
};
deadlines: {
applicationStart: string;
applicationEnd: string;
examDateTentative?: string;
};
officialLink: string;
verifiedStamp: boolean;
}Smart Rozgar
Sarkari job posts in Hindi and English, sorted by closing date, each linked to its source.
Written by
Dhruw Kumar Singh
Infrastructure & Reliability Lead at Nor AI Technologies
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