Novel Data Representation for Text Extraction from Multispectral Historical Document Images
The extraction and analysis of useful information from old document images is very important into cultural heritage preservation. In advanced research, where the goal is to separate the foreground (in general, text) from the background, image restoration and pattern classification techniques are used. Most of these methods consist of classifying the pixels based on their gray-scale value. In this paper, we propose to perform foreground pattern extraction using regions-of-interest (ROI) analysis and a maximum likelihood classifier designed for multispectral document images. As contribution, a new feature vector is proposed to improve discrimination between patterns that is embedded in a simple statistical classification method. The results, which are promising, are compared to the state-of-the-art.