  This paper proposes a new hierarchical MDL-based model for a joint detection
and classification of multilingual text lines in im- ages taken by hand-held
cameras. The majority of related text detec- tion methods assume alphabet-based
writing in a single language, e.g. in Latin. They use simple clustering
heuristics specific to such texts: prox- imity between letters within one line,
larger distance between separate lines, etc. We are interested in a
significantly more ambiguous problem where images combine alphabet and
logographic characters from multiple languages and typographic rules vary a lot
(e.g. English, Korean, and Chinese). Complexity of detecting and classifying
text lines in multiple languages calls for a more principled approach based on
information- theoretic principles. Our new MDL model includes data costs
combining geometric errors with classification likelihoods and a hierarchical
sparsity term based on label costs. This energy model can be efficiently
minimized by fusion moves. We demonstrate robustness of the proposed algorithm
on a large new database of multilingual text images collected in the pub- lic
transit system of Seoul.
