A pattern recognition approach to understanding the multi-layer perceptron
Pattern Recognition Letters
Off-line handwritten Korean character recognition based on stroke extraction and representation
Pattern Recognition Letters
Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition-based gesture spotting in video games
Pattern Recognition Letters
Character segmentation and recognition algorithm of text region in steel images
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
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In this paper, we propose a method of character extraction from documents in which both Hangul (Korean characters) and alphanumeric characters are written. In order to ensure accurate segmentation of touching characters, character segmentation and recognition are performed by turns. We use a recognizer to select the correct cutting point among candidates generated by the character segmenter. The character segmenter is implemented by a Multi-Layer Perceptron (MLP) trained by a back propagation algorithm. As the MLP has been trained for all kinds of touching characters, it can segment all touching characters by itself. Experimental results show that the proposed method achieves high segmentation rate in documents written in both Hangul and alphanumeric characters.