Commercializing AI Tech | United States Postal Service
The United States Postal Service
Artificial intelligence technology enables efficiency
Venu Govindaraju's work in handwriting recognition was at the center of the first handwritten address interpretation system used by the United States Postal Service (USPS). USPS issued a contract to researchers at the University at Buffalo to develop the handwriting recognition technology. One year after implementation it saved the USPS $90 million by automatically processing, and barcoding for precise delivery, more than 25 billion letters. The 2009 Computing Community Consortium dubbed the project as “one of the most successful applications of Machine Learning for developing a real-time engineered system.”
How The USPS Collaborated with UB
Handwriting Recognition Technology | Perfecting Multilingual OCR | Enabling Postal Automation
CHALLENGE & OPPORTUNITY
Revolutionizing handwriting understanding & automation
Venu Govindaraju and his team of more than 50 post-doc students and researchers pioneered the world's first self-governing system for handwriting understanding. Operating at 13 postal mail pieces per second, it interprets natural handwriting without strict rules or forms. Govindaraju's method employs advanced algorithms to determine destinations from 170 million possibilities using contextual information and generated lexicons. Overcoming challenges of natural cursive handwriting, they utilized an interactive A*-like algorithm and a stochastic recognizer to cluster and distinguish various writing styles.
SOLUTION & IMPACT
Expanding AI frontiers
Govindaraju's research impact extends far beyond revolutionizing the postal service industry, branching into Digital Libraries and Multilingual OCR. His methods facilitate early disease detection via healthcare form processing for the New York State (NYS) Department of Health, enhance patient safety through automated reading of medical prescriptions, and enable efficient access to historical documents, including Sanskrit and Arabic texts. Moreover, his techniques enable the retrieval of lecture video segments with significant handwritten and whiteboard content. Through innovations in word spotting, transcript mapping, text retrieval, and writer identification, Govindaraju has introduced powerful methods that advance technology across diverse applications.
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