Language Identification in Document Images

This paper presents a system dedicated to automatic language identification of text regions in heterogeneous and complex documents. This system is able to process documents with mixed printed and handwritten text and various layouts. To handle such a problem, we propose a system that performs the following sub-tasks: writing type identification (printed/handwritten), script identification and language identification. The methods for the writing type recognition and the script discrimination are based on the analysis of the connected components while the language identification approach relies on a statistical text analysis , which requires a recognition engine. We evaluate the system on a new public dataset and present detailed results on the three tasks. Our system outperforms the Google plug-in evaluated on the ground-truth transcriptions of the same dataset.