Large-scale digitization of manuscripts is facilitated by high-accuracy optical character recognition (OCR) engines. The focus of our work is on using these tools to digitize Latin texts. Many of the texts in the language, especially the early modern, make heavy use of special characters like ligatures and accented abbreviations. Current OCRs are inadequate for our purpose: their built-in train...

We propose TRIPWIRE as a framework that provides for the mediation of software self-awareness by supporting real-time assessment and response capabilities. Our approach is inspired by the recent success of automatic speech recognition systems, which can assess the likelihood of a potentially unbounded set of possible utterances and select the most likely candidate in real-time, given an underly...

This paper describes recent work towards development of new corpora and tools for Turkish speech research. This effort represents an on-going collaboration between the Center for Spoken Language Research (CSLR) at the University of Colorado and the Department of Electrical Engineering at the Middle East Technical University (METU). A new text corpus developed from Turkish newspapers’ text is de...

We survey recent mathematical and computational work in the eld of gene recognition, focusing on the techniques that have been developed to tackle the problem of identifying protein coding regions in genes. We also present a new approach to gene recognition which is based on a variety of tools we have developed.

In this paper, we newly introduce a collection of databases and evaluation tools called CENSREC-4, which is an evaluation framework for distant-talking speech under hands-free conditions. Distant-talking speech recognition is crucial for a handsfree speech interface. Therefore, we measured room impulse responses to investigate reverberant speech recognition in various environments. The data con...

Optical Character Recognition software (OCR) are important tools for obtaining accessible texts. We propose the use of artificial neural networks (ANN) in order to develop pattern recognition algorithms capable of recognizing both normal texts and formulae. We present an original improvement of the backpropagation algorithm. Moreover, we describe a novel image segmentation algorithm that exploi...

This work discusses an ongoing project for hands gesture recognition in computer vision systems. The proposed approach is based on the shape analysis tools introduced in [1]. More specifically, the wavelet transform will be used for generating features for the recognition of hand gestures based on contours.

• Implemented corrective training to improve recognition performance; on the standard training set this improves speaker-independent perplexity 60 performance from 6.7% error to 5.1% error, and for a larger training set (about 11,000 sentences), improves speaker-independent recognition from 5.3% error to 4.1% error. Plans • Complete the construction of the current hardware design, and develop s...

Despite the existence of effective methods that solve named entity recognition tasks for such widely used languages as English, there is no clear answer which methods are the most suitable for languages that are substantially different. In this paper we attempt to solve a named entity recognition task for Lithuanian, using a supervised machine learning approach and exploring different sets of f...