Recognizing Thousands of Legal Entities through Instance-based Visual Classification

Valentin Leveau (1, 2) , Alexis Joly (1) , Olivier Buisson (2) , Pierre Letessier (2) , Patrick Valduriez (1)

1 ZENITH - Scientific Data Management
2 INA - Institut National de l'Audiovisuel

This paper considers the problem of recognizing legal en-tities in visual contents in a similar way to named-entity recognizers for text documents. Whereas previous works were restricted to the recognition of a few tens of logotypes, we generalize the problem to the recognition of thousands of legal persons, each being modeled by a rich corporate identity automatically built from web images. We intro-duce a new geometrically-consistent instance-based classifi-cation method that is shown to outperform state-of-the-art techniques on several challenging datasets while being much more scalable. Further experiments performed on an au-tomatic web crawl of 5,824 legal entities demonstrates the scalability of the approach.

Domaines: Traitement du signal et de l'image [eess.SP]

Valentin Leveau, Alexis Joly, Olivier Buisson, Pierre Letessier, Patrick Valduriez. Recognizing Thousands of Legal Entities through Instance-based Visual Classification. MM: Conference on Multimedia, Nov 2014, Orlando, FL, United States. pp.1029-1032, ⟨10.1145/2647868.2655038⟩. ⟨hal-01077508⟩