Segmentation of touching characters using an MLP
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.
character segmentation neural network candidate cutting points mlp-based segmentation