Issue No. 11 - November (2008 vol. 30)
Tijn van der Zant , University of Groningen, Groningen
Lambert Schomaker , University of Groningen, Groningen
Koen Haak , University of Groningen, Groningen
For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language and collection. We propose a biologically inspired whole-word recognition method which is used to incrementally elicit word labels in a live, web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neuro-physiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows to classify text-images that have a low frequency of occurrence. Typically these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually standard pattern-recognition technology cannot deal with these text-images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.
Handwriting analysis, Interactive systems, Image/video retrieval, Computer vision, Computational neuroscience, Digital Libraries, Information Storage and Retrieval, Information Technology and Systems, Invariants, Feature Measurement, Image Processing and Computer Vision, Computing Methodologies
L. Schomaker, K. Haak and T. van der Zant, "Handwritten-Word Spotting Using Biologically Inspired Features," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 1945-1957, 2008.