The accuracy of Optical Character Recognition (OCR) technologies considerably impacts the way digital documents are indexed, accessed and exploited. Post-processing approaches detect and correct remaining errors to improve the quality of OCR texts. However, state-of-the-art approaches still need to be improved. Most of the existing post-OCR techniques use predefined error position lists or apply simple techniques to detect errors. In this paper, we describe a novel error detector using different features from character-level (including character noisy channel, index of peculiarity) to word-level (such as frequencies of n-grams, skip-grams, part-of-speech) Experimental results show that our approach outperforms the best performing techniques in the ICDAR 2017 Competition on Post-OCR text correction.

In this paper, we address multimodal indexing and retrieval for videos of lectures or seminars. This paper proposes a combination of technologies respectively issuing from image document analysis and text mining. Based on visual information and textual information extracted from slide images, we investigate a Bag of mixed Words (visual words and textual words) model to represent lecture slide's contents. Lecture videos are indexed and retrieved by using extended Bag of Words model. In this model, it is assumed that a video may contain multiple subjects; and this model discovers the visual representation of these subjects automatically and indexes the video accordingly. We discuss the mixed text/image query and proposed indexing approach for retrieval lecture videos and report a quantitative evaluation on lecture videos of our Lab.

This paper deals with cultural heritage preservation and ancient document indexing. In the management of historical documents, ancient images are described using semantic information, often manually annotated by historians. In this paper, we propose an approach to interactively propagate the historians' knowledge to a database of drop caps images manually populated by historians with drop caps image annotations. Based on a novel document indexing processing scheme which combines the use of the Zipf law and the use of bag of patterns, our approach extends the Bag of Words model to represent the knowledge by visual features through relevance feedback. Then annotation propagation is automatically performed to propagate knowledge to the drop caps image database. In this article, our approach is presented together with preliminary experimental results and an illustrative example.