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Segmentation is a major function in an OCR system. During this step, the main document components (text / graphic areas, text lines, words and characters or glyphs) are automatically extracted.

Traditionally, segmenting historical machine-printed documents has been tackled by the use of techniques that are mainly designed for contemporary documents.

As a result, several problems inherent in historical documents such as general low quality of the original volume; complex, dense and irregular layouts; artefacts not completely corrected during pre-processing (noise between characters, ink diffusion and text skew) seriously affect the segmentation and, consequently, the recognition accuracy of OCR. Furthermore, volume-specific rules are usually used for segmenting historical machine-printed documents. In the context of a mass digitisation workflow, this is unworkable and has necessitated the development of new approaches.

IMPACT introduces novel hierarchical segmentation models that allow the discrete problems of text block, text line, word and character segmentation to be addressed separately while at the same time allowing for interplay between all levels.