This article 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, the authors propose a system that performs the following sub-tasks: writing type identification (printed/handwritten), script identification and language identification. The methods for writing type recognition and script discrimination are based on analysis of the connected components, while the language identification approach relies on a statistical text analysis, which requires a recognition engine. The authors evaluate the system on a new public dataset and present detailed results on the three tasks. Their system outperforms the Google plug-in evaluated on ground-truth transcriptions of the same dataset. c 2016 Society for Imaging Science and Technology.