Structure-preserving document image compression

Maintaining a document in image form is often preferable in order to avoid the high cost of manual conversion or the introduction of large numbers of errors by automatic OCR and/or graphics interpretation. The large volume of data in the image can be greatly reduced by using compression techniques. Text-intensive document images typically have a great deal of redundancy in the bitmap representations of symbols, and we make use of that redundancy for compression by clustering components, representing each cluster by a template and encoding the error. Our method is novel in modeling the error associated with each cluster and in preserving structure, an important component for readability and processing.