Improvement of Omnipage 18´s efficiency

In many Botanical Museums in the world exist millions of dried plants on paper sheets. They were collected in the last two hundred years. The collectors left annotations about the plants on the sheets. Some of them are written in handwriting others by a typewriter. There exist also comments on printed forms which are glued on the sheets. By an automatic OCR of the scanned herbarium objects the recognized texts can be put into a database. Commercial OCR-programs like Finereader or Omnipage are capable to recognize undisturbed printed texts in a correct way. Unfortunately we deal with historical material which often contains overwritten or crossed out words, the paper is yellowed, molded and has other artifacts. Moreover it seems to be extremely complicated to detect text regions in a complex environment with objects like roots, leaves, stamps, bar codes, yardsticks, color charts etc. The old writing leads to read errors which are not the biggest problem, because they can be compensated by tolerant database queries. Worse is that sometimes text regions cannot be localized. In this paper a method is presented which helps Omnipage to detect missed text and also can distinguish printed text from handwriting.