Text segmentation in mixed-mode images using classification trees and transform tree-structured vector quantization

Multimedia applications such as educational videos and color facsimile contain images that are rich in both textual and continuous tone data. Because these two types of data have different properties, segmentation of the images into text and continuous tone data can improve compression by allowing different compression parameters or even algorithms to be employed on the different types. We propose and compare algorithms that use classification trees (CLTR) or tree-structured vector quantization (TSVQ) for block-based classification in mixed-mode images. We also examine different types of features that can be used in these classifiers. The results show that using linear transform features with either the CLTR or TSVQ can be effective for accurate text classification. In addition, the results indicate that combining these classifiers with another TSVQ that is designed simultaneously to minimize both compression and classification error can provide better classification than does either system alone