In this work a Gaussian Hidden Markov Model (GHMM) based automatic sign language recognition system is built on the SIGNUM database. The system is trained on appearance-based feat...
We consider a model for which it is important, early in processing, to estimate some variables with high precision, but perhaps at relatively low recall. If some variables can be ...
Gary B. Huang, Andrew Kae, Carl Doersch, Erik G. L...
—A method for locating mathematical expressions in document images without the use of optical character recognition is presented. An index of document regions is produced from re...
—We propose a new method for an effective removal of the printing artifacts occurring in historical newspapers which are caused by problems in the hot metal typesetting, a widely...
Iuliu Vasile Konya, Stefan Eickeler, Christoph Sei...
—The italic detection and slant rectification is a key step of optical character recognition (OCR). In this paper, a novel method is proposed to detect and rectify italic charact...
A framework is presented for discovering partial duplicates in large collections of scanned books with optical character recognition (OCR) errors. Each book in the collection is r...
— For Optical Character Recognition (OCR) of bilingual or multilingual document containing text words in regional language and numerals in English, it is necessary to identify di...
In natural scene, text elements are corrupted by many types of noise, such as streaks, highlights, or cracks. These effects make the clean and automatic segmentation very difficu...
Errors are unavoidable in advanced computer vision applications such as optical character recognition, and the noise induced by these errors presents a serious challenge to downstr...
In this paper, we analyze the performance of name finding in the context of a variety of automatic speech recognition (ASR) systems and in the context of one optical character rec...
David R. H. Miller, Sean Boisen, Richard M. Schwar...