Joint Energy-based Detection and Classificationon of Multilingual Text Lines
This addresses the ambiguous problem of multilingual text detection and classification in real-world images, which is incremental as it extends beyond single-language methods to handle multiple languages and typographic variations.
The paper tackles the problem of jointly detecting and classifying multilingual text lines in images, which combine alphabet and logographic characters from languages like English, Korean, and Chinese, by proposing a hierarchical MDL-based model that minimizes an energy function with data costs and sparsity terms. It demonstrates robustness on a new database of multilingual text images from Seoul's public transit system.
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.