Showing papers by "Michael Blumenstein published in 1999"

A new segmentation algorithm for handwritten word recognition

Abstract: An algorithm for segmenting unconstrained printed and cursive words is proposed The algorithm initially oversegments handwritten word images (for training and testing) using heuristics and feature detection An artificial neural network (ANN) is then trained with global features extracted from segmentation points found in words designated for training Segmentation points located in "test" word images are subsequently extracted and verified using the trained ANN Two major sets of experiments were conducted, resulting in segmentation accuracies of 7506% and 7652% The handwritten words used for experimentation were taken from the CEDAR CD-ROM The results obtained for segmentation can easily be used for comparison with other researchers using the same benchmark database

Neural-based solutions for the segmentation and recognition of difficult handwritten words from a benchmark database

Abstract: A new intelligent segmentation technique is proposed that may be used in conjunction with a neural classifier and a simple lexicon for the recognition of difficult handwritten words. A heuristic segmentation algorithm is initially used to over-segment each word. An artificial neural network (ANN) trained with 32,034 segmentation points is then used to verify the validity of the segmentation points found. Following segmentation, character matrices from each word are extracted, normalised and then passed through a global feature extractor, after which a second ANN trained with segmented characters is used for classification. These recognised characters are grouped into words and presented to a variable-length lexicon that utilises a string processing algorithm to compare and retrieve those words with the highest confidences. This research provides promising results for segmentation, character and word recognition.

A New Compression Technique Using an Artificial Neural Network

Abstract: In this paper, we present a direct solution method based neural network for image compression. The proposed technique includes steps to break down large images into smaller windows and eliminate redundant information. Furthermore, the technique employs a neural network trained by a non-iterative, direct solution method. An error backpropagation algorithm is also used to train the neural network, and both training algorithms are compared. The proposed technique has been implemented in C on the SP2 Supercomputer. A number of experiments have been conducted. The results obtained, such as compression ratio and transfer time of the compressed images are presented in this paper.