M
Michael Blumenstein
Researcher at University of Technology, Sydney
Publications - 343
Citations - 5826
Michael Blumenstein is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Feature extraction & Handwriting recognition. The author has an hindex of 37, co-authored 328 publications receiving 4764 citations. Previous affiliations of Michael Blumenstein include Commonwealth Scientific and Industrial Research Organisation & Australian Artificial Intelligence Institute.
Papers
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Journal ArticleDOI
A Segmentation Algorithm Used in Conjunction with Artificial Neural Networks for the Recognition of Real-World Postal Addresses
TL;DR: The segmentation algorithm is used as a preprocessing technique to p repare raw training data for use with an Artificial Neural Network and has been successfully tested on real world handwritten postal addresses.
Artificial Neural Network Based Segmentation Algorithm for Off-line Handwriting Recognition
TL;DR: A method for segmentation of difficult handwriting with the use of conventional algorithms in conjunction with ANNs, which is heuristic in nature detecting important features which may represent a prospective segmentation point.
Proceedings ArticleDOI
Multi-angle based lively sclera biometrics at a distance
TL;DR: This piece of work proposes a liveliness based sclera eye biometric, validation and recognition technique at a distance to obtain higher accuracy and to incorporate liveliness in face recognition.
Proceedings ArticleDOI
Person Head Detection in Multiple Scales Using Deep Convolutional Neural Networks
TL;DR: The purpose of this study is to detect human heads in natural scenes acquired from a publicly available dataset of Hollywood movies by using state-of-the-art object detectors based on deep convolutional neural networks.
Proceedings ArticleDOI
Experimental analysis of the modified direction feature for cursive character recognition
X.Y. Liu,Michael Blumenstein +1 more
TL;DR: The modified direction feature (MDF) extraction technique builds upon a previous technique proposed by the authors that extracts direction information from the structure of character contours and is extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image.