Building reading machines with Artificial Intelligence.

Transcription

1 Building reading machines with Artificial Intelligence

2 IDEA AND MISSION N.I.Te. arises from an entrepreneurial idea of research team of Natural Computational Lab of University of Salerno, Italy and now it is an innovative SME working in ICT sector of Pattern Recognition and Artificial Intelligence N.I.Te. s mission is to provide state-of-the-art innovative software technologies and advanced solutions by using Artificial Intelligence algorithms

3 Problem Convert digital handwritten document images into machine readable text for reading, indexing and searching Assemblea Costituente Art.4 L Italia rinuncia alla guerra come strumento di politica nazionale e respinge ogni imperialismo e ogni adesione a blocchi imperialistici. Accetta e propugna, a condizione di reciprocità e di uguaglianza, qualsiasi limitazione di sovranità, che sia necessaria ad un ordinamento internazionale di pace, di giustizia e di unione tra i popoli. Valiani

4 Problem size 1 Bn Pages / every day manually processed $ 6.5 Bn /annual payroll for companies Only in US A2IA White Paper

5 Solution A proprietary software technology able to read, search and analyze handwriting and capable to save an average percentage of 60% of time and cost needed to manually perform those tasks 75 % Average Accuracy

6 Technology read search analyze writer alphabet language

7 Market Size Image Recognition Software $ 9.65B OCR - ICR CAGR + 21% $ 25.65B Report Markets & Markets

8 Competitors

9 THE SOLUTIONS Our software solutions are: MASQUERADE software for analysis and examination of morphological and dynamic features of the handwriting APPOST software for automatic cursive handwriting recognition in structured and unstructured digital documents HANDBIBLIO software for automatic searching and indexing into huge size digital handwritten collections (ancient and modern manuscripts) Data Analysis - software for automatic analysis of huge size dataset (Big Data) by using advanced Machine Learning techniques to identify trends and make forecasting

10 VERTICALS & CUSTOMERS > 10 Customers & Partners in 5 different states Cybersecurity Handwriting analysis Legal /HR sector Postal Sector Insurance & Banking Healthcare Regulation Sector Cultural Heritage Public Libraries Private companies

11 CASE STUDIES Milano Firenze Bari Intestatario Importo wikimedia 100 xyz Handwritten Documents Search and Retrieve

12 Business Model 1) On premise Access Fee + Customization Fee Variable 2) AIaaS on Cloud Access Fee + Service Fee (per Page )

13 Team Adolfo CEO Master Air Traffic Control 9 years in Artificial Intelligence Antonio CTO PhD Student 7 years in Artificial Intelligence Canio Software Engineer 3 years in UI/UX Java Advisory Board Rosa R&D PhD 7 years in Neural Models Angelo IP Manager Full Professor 25 years in Artificial Intelligence Claudio Project Manager Full Professor 20 years in Artificial Intelligence Gianni Advisor 30 years in Management of Mobile and Software Companies

14 Roadmap 1Q/2012 Patent Deposit 4Q/2013 Private Beta Version 2Q/2015 Italian Patent Granted 3Q/2015 Patent Extension PCT 2Q/2017 American Patent Granted 3Q/2017 Break Even Point 2Q/2018 Israeli Patent Granted 4Q/2018 HCR AI aas Partnerships 2012/5 Company Foundation 4Q/2012 First Partnership Signed 2Q/2014 TechPeaks Accelerator Program 2Q/2015 Masquerade Launch 1Q/2016 International Customers 3Q/2017 EU Funding H2020 Step 1 1Q/2018 PoC in the Postal Sector 1Q/2018 PoC in the Insurance Sector 4Q/2018 EU Proposal H2020 Step 2

15 Awards & Prizes

16 Vision Make available and searchable all the knowledge contained in handwritten digital documents of the world

17 Building reading machines with Artificial Intelligence