Text lines and snippets extraction for 19th century handwriting documents<br /> layout analysis

International audienceIn this paper we propose a new approach to improve electronic editions of human science corpus, providing an efficient estimation of manuscripts pages structure. In any handwriting documents analysis process, the text line segmentation is an important stage. The presence of variable inter-line spaces, of inconstant base-line skews, overlapping and occlusions in unconstrained ancient 19th handwritten documents complexifies the text lines segmentation task. In this paper, we only use as prior knowledge of script the fact that text lines skews can be random and irregular. In that context, we model text line detection as an image segmentation problem by enhancing text line structure using Hough transform and a clustering of connected components so as to make text line boundaries appear. The proposed approach of snippets decomposition for page layout analysis lies on a first step of content pages classification in five visual and genetic taxonomies, and a second step of text line extraction and snippets decomposition. Experiments show that the proposed method achieves high accuracy for detecting text lines in regular and semi-regular handwritten pages in the corpus of digitized Flaubert manuscripts ("Dossiers documentaires de Bouvard et Pécuchet", 1872-1880)

Hierarchical decomposition of handwritten<br /> manuscripts layouts

http://www.springerlink.com/content/k6741wt1028l7310/International audienceIn this paper we propose a new approach to improve electronic editions of literary corpus, providing an efficient estimation of manuscripts pages structure. In any handwriting documents analysis process, structure recognition is an important issue. The presence of variable inter-line spaces, of inconstant base-line skews, overlappings and occlusions in unconstrained ancient 19th handwritten documents complicates the structure recognition task. Text line and fragment extraction is basedon the connexity labelling of the adjacency graph at different resolutionlevels, for borders, lines and fragments extraction

A hybrid approach for line segmentation in handwritten documents

This paper presents an approach for text line segmentation which combines connected component based and projection based information to take advantage of aspects of both methods. The proposed system finds baselines of each connected component. Lines are detected by grouping baselines of connected components belonging to each line by projection information. Components are assigned to lines according to different distance metrics with respect to their size. This study is one of the rare studies that apply line segmentation to Ottoman documents. Further, it proposes a new method, Fourier curve fitting, to detect the peaks in a projection profile. The algorithm is demonstrated on different printed and handwritten Ottoman datasets. Results show that the method manages to segment lines both from printed and handwritten documents under different writing conditions at least with 92% accuracy. © 2012 IEEE

Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance

A New Approach for line segmentation in Punjabi Language using Strip based Projection profile method

Abstract

Segmentation based Ottoman text and matching based Kufic image analysis

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical references leaves 80-88.Large archives of historical documents attract many researchers from all around the world. The increasing demand to access those archives makes automatic retrieval and recognition of historical documents crucial. Ottoman archives are one of the largest collections of historical documents. Although Ottoman is not a currently spoken language, many researchers from all around the world are interested in accessing the archived material. This thesis proposes two Ottoman document analysis studies; first one is a crucial pre-processing task for retrieval and recognition which is segmentation of documents. Second one is a more specific retrieval and recognition problem which aims matching Islamic patterns is Kufic images. For the first segmentation task, layout, line and word segmentation is studied. Layout segmentation is obtained via Log-Gabor filtering. Four different algorithms are proposed for line segmentation and finally a simple morphological method is preferred for word segmentation. Datasets are constructed with documents from both Ottoman and other languages (English, Greek and Bangla) to test the script-independency of the methods. Experiments show that our segmentation steps give satisfactory results. The second task aims to detect Islamic patterns in Kufic images. The sub-patterns are considered as basic units and matching is used for the analysis. Graphs are preferred to represent subpatterns where graph and sub-graph isomorphism are used for matching them. Kufic images are analyzed in three different ways. Given a query pattern, all the instances of the query can be found through retrieval. Going further, through known patterns images can be automatically labeled in the entire dataset. Finally, patterns that repeat inside an image can be automatically discovered. As there is no existing Kufic dataset, a new one is constructed by collecting images from the Internet and promising results are obtained on this dataset.Adıgüzel, HandeM.S