Despite several decades of research in handwriting recognition, the goal of having computers access handwritten information from unconstrained document images is still elusive. Current handwriting recognition systems are only capable of recognizing words that are present in a restricted lexicon typically comprised of 10 to 1000 words. As the size of the lexicon grows, the recognition accuracy falls sharply and is reported to be around 30% for a10K word lexicon. The objective of this research is to raise the accuracy levels on unconstrained handwritten documents by reducing the size of lexicons. We present an innovative method of lexicon reduction by topic categorization of handwritten documents. After categorization of a document into a topic e.g. sports, science etc. we use smaller lexicons that include only words with high mutual information with that topic and hence increase performance of recognizers. In this paper we present different techniques and report results on a publicly available dataset.

We consider the problem of diagnosing performance problems in distributed system and networks given end-to-end performance measurements provided by test transactions, or probes. Common techniques for problem diagnosis such as, for example, codebook and network tomography usually assume a known dependency (e.g., routing) matrix that describes how each probe depends on the systems components. However, collecting full information about routing and/or probe dependencies on all systems components can be very costly, if not impossible, in large-scale, dynamic networks and distributed systems. We propose an approach to problem diagnosis and dependency discovery from end-to-end performance measurements in cases when the dependency/routing information is unknown or partially known. Our method is based on Blind Source Separation (BSS) approach that aims at reconstructing unobserved input signals and the mixing-weights matrix from the observed mixtures of signals. Particularly, we apply sparse non-negative matrix factorization techniques that appear particularly fitted to the problem of recovering network bottlenecks and dependency (routing) matrix, and show promising experimental results on several realistic network topologies.