The SCRIBO Module of the Olena Platform: A Free Software Framework for Document Image Analysis

Abstract

Abstract-Electronic documents are being more and more usable thanks to better and more affordable network, storage and computational facilities. But in order to benefit from computeraided document management, paper documents must be digitized and analyzed. This task may be challenging at several levels. Data may be of multiple types thus requiring different adapted processing chains. The tools to be developed should also take into account the needs and knowledge of users, ranging from a simple graphical application to a complete programming framework. Finally, the data sets to process may be large. In this paper, we expose a set of features that a Document Image Analysis framework should provide to handle the previous issues. In particular, a good strategy to address both flexibility and efficiency issues is the Generic Programming (GP) paradigm. These ideas are implemented as an open source module, SCRIBO, built on top of Olena, a generic and efficient image processing platform. Our solution features services such as preprocessing filters, text detection, page segmentation and document reconstruction (as XML, PDF or HTML documents). This framework, composed of reusable software components, can be used to create full-fledged graphical applications, small utilities, or processing chains to be integrated into third-party projects.

Planting, Growing, and Pruning Trees: Connected Filters Applied to Document Image Analysis

“…Therefore, the tools presented in this paper will be available in the next release of the Scribo module [21], as part of the Olena image processing platform [22], [23], along with an online demonstrator.…”

Layout analysis and content enrichment of digitized books

“…Chen et al [9] proposes a method based on whitespace rectangles extraction and grouping: initially the foreground CCs are extracted and linked into chains according to their horizontal adjacency relationship; whitespace rectangles are then extracted from the gap between horizontally adjacent CCs; progressively CCs and whitespaces are grouped and filtered to form text lines and afterward text blocks. Lazzara et al [20] provides a chain of steps to first recognize text regions and successively non-text elements. Foreground CCs are extracted, then delimiters (such as lines, whitespaces and tab-stop) are detected with object alignment and morphological algorithms.…”

Illustrations Segmentation in Digitized Documents Using Local Correlation Features

“…Chen et al 14 proposes a method based on whitespace rectangles extraction and grouping: initially the foreground CCs are extracted and linked into chains according to their horizontal adjacency relationship; whitespace rectangles are then extracted from the gap between horizontally adjacent CCs; progressively CCs and whitespaces are grouped and filtered to form text lines and afterward text blocks. Lazzara et al 15 provides a chain of steps to first recognize text regions and successively non-text elements. Foreground CCs are extracted, then delimiters (such as lines, whitespaces and tab-stop) are detected with object alignment and morphological algorithms.…”