Alessandro Vinciarelli Machine Learning for Audio

Machine Learning for Audio, Image and Video Analysis - Theory and Applications

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Cursive character recognition by learning vector quantization

This paper presents a cursive character recognizer embedded in an o€-line cursive script recognition system. The recognizer is composed of two modules: the ®rst one is a feature extractor, the second one a learning vector quantizer. The selected feature set was compared to Zernike polynomials using the same classi®er. Experiments are reported on a database of about 49,000 isolated characters.

1 Machine Learning Methods for Social Signal Processing

In this chapter we focus on systematization, analysis, and discussion of recent trends in machine learning methods for Social signal processing (SSP)(Pentland 2007). Because social signaling is often of central importance to subconscious decision making that affects everyday tasks (e.g., decisions about risks and rewards, resource utilization, or interpersonal relationships) the need for automa...

Cursive Character Challenge: a New Database for Machine Learning and Pattern Recognition

Cursive character recognition is a challenging task due to high variability and intrinsic ambiguity of cursive letters. This paper presents C-Cube (Cursive Character Challenge), a new public-domain cursive character database. C-Cube contains 57293 cursive characters manually extracted from cursive handwritten words, including both upper and lower case versions of each letter. The database can b...

Information Retrieval on Noisy Text