Ancient Documents Denoising and Decomposition Using Aujol and Chambolle Algorithm

With the improvement of printing technology since the 15th century, there is a huge amount of printed documents published and distributed. These documents are degraded by the time and require to be preprocessed before being submitted to image indexing strategy, in order to enhance the quality of images. This paper proposes a new pre-processing that permits to denoise these documents, by using a Aujol and Chambolle algorithm. Aujol and Chambolle algorithm allows to extract meaningful components from image. In this case, we can extract shapes, textures and noise. Some examples of specific processings applied on each layer are illustrated in this paper

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols

Interpretation, Evaluation and the Semantic Gap ... What if we Were on a Side-Track?

International audienceA significant amount of research in Document Image Analysis, and Machine Perception in general, relies on the extraction and analysis of signal cues with the goal of interpreting them into higher level information. This paper gives an overview on how this interpretation process is usually considered, and how the research communities proceed in evaluating existing approaches and methods developed for realizing these processes. Evaluation being an essential part to measuring the quality of research and assessing the progress of the state-of-the art, our work aims at showing that classical evaluation methods are not necessarily well suited for interpretation problems, or, at least, that they introduce a strong bias, not necessarily visible at first sight, and that new ways of comparing methods and measuring performance are necessary. It also shows that the infamous {\em Semantic Gap} seems to be an inherent and unavoidable part of the general interpretation process, especially when considered within the framework of traditional evaluation. The use of Formal Concept Analysis is put forward to leverage these limitations into a new tool to the analysis and comparison of interpretation contexts

SentiML++ : An Extension of the SentiML Sentiment Annotation Scheme

International audienceIn this paper, we propose SentiML++, an extension of SentiML with a focus on annotating opinions answering aspects of the general question \who has what opinion about whom in which context?". A detailed comparison with SentiML and other existing annotation schemes is also presented. The data collection annotated with SentiML has also been annotated withSentiML++ and is available for download for research purpose

Extraction of Ancient Map Contents Using Trees of Connected Components

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Extraction of ancient maps content by using trees of connected components

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Dichotomic lattices and local discretization for Galois lattices

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