Synaptics has applied its extensive expertise in neural networks research to real-world pattern recognition problems, such as object recognition, handwriting recognition, and document processing.

Pattern recognition algorithms are designed to make predictions from complex information by classifying the input patterns into predefined categories. The research at Synaptics focuses on applying both empirical and knowledge - based learning techniques to solve human interface problems. This research in Pattern Recognition is very broad-based--it spans a spectrum of learning models, including those based on statistics, neural networks, support vector machines, radial basis functions, and heuristics.

The task of the pattern recognition algorithms is to identify patterns in user input to predict and classify the input. This research has been applied to create software that can recognize and identify handwritten Asian characters, thereby transforming them into a form useful for computer processing. This character recognition capability is gaining increasing importance in mobile electronic devices, such as PDA's and cell phones.