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.

This paper presents a novel approach for language identification in handwritten documents. The approach is based on script identification followed by character recognition. BLSTM-CTC based handwriting recognizers are used and the OCR output is fed to a statistical language identifier for detecting the language of the input handwritten document. Documents in two scripts (Latin and Bengali) and four languages (English, French, Bengali and Assamese) are considered for evaluation. Several alternative frameworks have been explored, effects of handwriting recognition and text length on language detection have been studied. It is observed that with some empirical restrictions it is very much possible to achieve more that 80% language detection accuracy and based on the current research practical systems can be designed.

This paper presents a Document Image Analysis (DIA) system able to extract homogeneous typed and handwritten text regions from complex layout documents of various types. The method is based on two connected component classification stages that successively discriminate text/non text and typed/handwritten shapes, followed by an original block segmentation method based on white rectangles detection. We present the results obtained by the system during the first competition round of the MAURDOR campaign.

RÉSUMÉ. Dans cet article, nous présentons les résultats obtenus par un détecteur de tableau dans le cadre des campagnes MAURDOR, pour lesquelles le corpus présente la particularité de contenir des document fortement hétérogènes dans leur mise en page, leurs scripts et les langues utilisées. ABSTRACT. This paper presents the results obtained by a table detector during the MAURDOR campaign, the corpus of which contain heterogeneous documents in French, English and Arabic with various types of table structures. MOTS-CLÉS : Détection de lignes, Détection de tableaux, Maurdor

RÉSUMÉ. Dans cet article, nous présentons un système de segmentation des zones de texte imprimées ou manuscrites dans des documents complexes. La méthode réalise une première classification des composantes connexes en tant que texte/non-texte, puis un deuxième étage discrimine les composantes manuscrites des composantes imprimées. Les composantes de texte sont ensuite regroupées en blocs homogènes à l’aide d’un algorithme basé sur la détection des rectangles blancs. Nous présentons les résultats obtenus par le système lors de la première campagne MAURDOR.