Text line segmentation for gray scale historical document images

In this paper we present a new approach for text line segmentation that works directly on gray-scale document images. Our algorithm constructs distance transform directly on the gray-scale images, which is used to compute two types of seams: medial seams and separating seams. A medial seam is a chain of pixels that crosses the text area of a text line and a separating seam is a path that passes between two consecutive rows. The medial seam determines a text line and the separating seams define the upper and lower boundaries of the text line. The medial and separating seams propagate according to energy maps, which are defined based on the constructed distance transform. We have performed various experimental results on different datasets and received encouraging results

Layout Analysis for Arabic Historical Document Images Using Machine Learning

Page layout analysis is a fundamental step of any document image understanding system. We introduce an approach that segments text appearing in page mar-gins (a.k.a side-notes text) from manuscripts with com-plex layout format. Simple and discriminative features are extracted in a connected-component level and sub-sequently robust feature vectors are generated. Multi-layer perception classifier is exploited to classify con-nected components to the relevant class of text. A voting scheme is then applied to refine the resulting segmenta-tion and produce the final classification. In contrast to state-of-the-art segmentation approaches, this method is independent of block segmentation, as well as pixel level analysis. The proposed method has been trained and tested on a dataset that contains a variety of com-plex side-notes layout formats, achieving a segmenta-tion accuracy of about 95%.

WebGT: An Interactive Web-based System for Historical Document Ground Truth Generation

Abstract—We present WebGT, the first web-based system to help users produce ground truth data for document images. This user-friendly software system helps historians and computer scientists collectively annotate historical documents. It supports real time collaboration among remote sites independent of the local operating system and also provides several novel semiautomatic tools that have proven effective for annotating degraded documents. I

User-assisted alignment of arabic historical manuscripts

This work aims to simplify the tiresome manual comparison of two similar Arabic historical manuscripts. We developed a system that determines the difference between two manuscripts by comparing their components, while ignoring page breaks and different warping among consecutive rows; i.e., we treat each manuscript as one long row of components. We compare two components (blocks of pixels) by extracting features from the columns of their bounding rectangles. We adopted the edit distance, which is computed using dynamic time warping (DTW) on the feature domain, to measure similarity between components. The user selects the region to align in two manuscripts and the system return its alignment with visual clues that indicate the distance between the aligned components. In our current implementation, our system provides good results and requires less interaction for manuscripts at good quality that do not include touching components. We tested our system on different Arabic manuscripts of various qualities and received encouraging results