In this presentation we describe a methodology we have adopted for coding the semantic structure of a historical document and the resulting semantic model. To do this, we adapted currently available methodologies for ontology engineering to the context of semantic document coding. Using Protégé-2000 we then used this methodology to develop a formal ontological model and finally to encode a hist...

The collections of historical books are an important source of information, both for the history of previous periods and for the development of the cultural documentation itself. Although to date, there have been made several attempts of digitalization and electronic navigation, there is not an appropriate frame of optical process and analysis of the content of these collections, consequently a...

English. Language segmentation, i.e. the division of a multilingual text into monolingual fragments has been addressed in the past, but its application to historical documents has been largely unexplored. We propose a method for language segmentation for multilingual historical documents. For documents that contain a mix of highand low-resource languages, we leverage the high availability of hi...

We describe CITlab’s recognition system for the HTRtS competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises the recognition of historical handwritten documents. The core algorithms of our system are based on multidimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software m...

Indexing and searching collections of handwritten archival documents and manuscripts has always been a challenge because handwriting recognizers do not perform well on such noisy documents. Given a collection of documents written by a single author (or a few authors), one can apply a technique called word spotting. The approach is to cluster word images based on their visual appearance, after s...

The amount of digitized legacy documents has been rising dramatically over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents. The vast majority of them remain waiting to be transcribed into a textual electronic format (such as ASCII or PDF) that would provide historians and other researchers new ways of indexing, consulting and que...

We present multi-column text region identification support for Ocular, the unsupervised historical printed document transcription project of Berg-Kirkpatrick et. al (2013). We use structured prediction with rich features defined on the input document and incorporate a transition model based on prior document layout assumptions. Our model is trained using a structured-SVM objective on a randomly...

Historical documents play a vital role in understanding our past and hence need to be preserved. Over the period, these documents tend to possess many variations like stains, strain, ink seepage, dust etc. Image enhancement techniques can be utilized to improve the quality of these images by removing noise and increasing contrast range. The proposed method mainly deals with enhancing the histor...

We describe our work on text-image alignment in context of building a historical document retrieval system. We aim at aligning images of words in handwritten lines with their text transcriptions. The images of handwritten lines are automatically segmented from the scanned pages of historical documents and then manually transcribed. To train automatic routines to detect words in an image of hand...