The digital conversion of documents (books, forms, old archives, correspondences, etc.) is a worldwide initiative and considerable amount of effort is invested to protect and conserve these documents. Producing digital copies gives the opportunity to a larger audience to access those documents and perform regular operations like searching (e.g. word spotting, see below), editing, conversion, publishing, etc. Our aim is to propose and develop top-notch solutions to help this digitization process by offering solutions in image processing, document layout analysis and handwriting recognition in a multi-script environment (Roman, Arabic, Bangla).
While modern models usually rely on machine learning and a significant amount of training data, another research focus of the group is to alleviate the data demand of document analysis models. By exploiting techniques such as transfer-, semi- or weakly supervised learning the application of machine learning models becomes easier and in the best case does not require any manually labelled data (see annotion-free learning).