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We are investigating techniques for deriving canonical stroke frequency descriptors from handwritten text to identify writers.We demonstrated that a relatively small set of canonical strokes can be successfully employed for generating discriminative frequency descriptors. Offline signature recognition is an important form of biometric identification that can be used for various purposes.It is almost always the case that scanned documents suffer from some form of degradation.
A short description of recent and ongoing activities in the lab is provided below.
Research on medical imaging is done in conjunction with researchers in the radiology department at the university of Chicago and the biomedical engineering department at IIT.
The Visual Computing Lab is a research group within the Computer Science department which is directed by Dr. The mission of the lab is to conduct research in advanced computational techniques concerning Visual Computing.
Visual Computing is a broad term that encompasses several areas in computer science including: computer vision, computer graphics, image and video processing, geometric modeling, and pattern recognition.
We are developing statistical techniques for degraded document image enhancement in this context. Writer identification in offline handwritten documents is a difficult task with multiple applications such as authentication, identification, and clustering in document collections.
For example, in the context of content-based document image retrieval, given a document with handwritten annotations it is possible to determine whether the comments were added by a specific individual and find other documents annotated by the same person.
Large collections of such complex documents are commonly found in legal and security investigations.
We have developed a test collection of several million documents and a prototype system to ingest it. There are numerous collections of scanned documents.
Ideally, vessel enhancement filters should enhance vessels and vessel junctions while suppressing nodules and other non-vessel elements.
We developed probabilistic vessel models from which novel vessel enhancement filters capable of enhancing junctions while suppressing nodules are derived.