On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Word Spotting: A New Approach to Indexing Handwriting
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Towards Whole-Book Recognition
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
Knowledge technologies for the social semantic desktop
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Ontology-based information extraction: An introduction and a survey of current approaches
Journal of Information Science
iDocument: using ontologies for extracting and annotating information from unstructured text
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Ontology-Based Information Extraction from Handwritten Documents
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Hi-index | 0.10 |
In this paper we introduce a new layer for the task of handwriting recognition (HWR), i.e., the use of semantic information in form of Resource Description Framework (RDF) knowledge bases. In particular, two novel processing stages are proposed for the first time in literature. The first stage is the inclusion of RDF knowledge bases into the HWR process, where we make use of a person's mental model. This process can be extended to use other ontological resource. The second stage is the transition from pure handwriting recognition to understanding the handwritten notes, i.e., the system extracts knowledge employing RDF knowledge-bases. This is also called ontology-based information extraction (OBIE). The task of our recognizer therefore is not only to recognize the ASCII transcription of the handwritten document, but also to identify the semantic concepts which appear in the text. For both novel approaches we performed a set of experiments on various data. First, the recognition rate of the HWR system is increased on several documents. Second, the performance of information extraction is also remarkable. By using the k-best word recognition alternatives in form of a lattice as an input for the OBIE system, the performance reaches a level which is very close to OBIE applied on pure ASCII text.