|
R. Bertrand, P. Gomez-Krämer, Oriol Ramos Terrades, P. Franco and Jean-Marc Ogier. 2013. A System Based On Intrinsic Features for Fraudulent Document Detection. 12th International Conference on Document Analysis and Recognition.106–110.
Abstract: Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer froma lack of security.
However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one.
In this paper, we present an automatic forgery detection method based on document’s intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set iscomputed for all characters. Then, based on a distance between characters of the same class.
Keywords: paper document; document analysis; fraudulent document; forgery; fake
|
|
|
Muhammad Muzzamil Luqman, Jean-Yves Ramel, Josep Llados and Thierry Brouard. 2013. Fuzzy Multilevel Graph Embedding. PR, 46(2), 551–565.
Abstract: Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs.
Keywords: Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic
|
|
|
Salvatore Tabbone and Oriol Ramos Terrades. 2014. An Overview of Symbol Recognition. In D. Doermann and K. Tombre, eds. Handbook of Document Image Processing and Recognition. Springer London, 523–551.
Abstract: According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity.
Keywords: Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting
|
|
|
Marçal Rusiñol and Josep Llados. 2009. A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices. IJDAR, 12(2), 83–96.
Abstract: Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors.
Keywords: Performance evaluation; Symbol Spotting; Graphics Recognition
|
|
|
Anjan Dutta, Josep Llados, Horst Bunke and Umapada Pal. 2014. A Product Graph Based Method for Dual Subgraph Matching Applied to Symbol Spotting. In Bart Lamiroy and Jean-Marc Ogier, eds. Graphics Recognition. Current Trends and Challenges. Springer Berlin Heidelberg, 7–11. (LNCS.)
Abstract: Product graph has been shown as a way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. Here we focus on the two major limitations of the previous version of the algorithm: (1) spurious nodes and edges in the graph representation and (2) inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual edge graph representation on the original graph representing the graphical information and the product graph is computed between the dual edge graphs of the pattern graph and the target graph. The dual edge graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates the pair of similar edges of two operand graphs and exponentiating the adjacency matrix finds similar random walks of greater lengths. Nodes joining similar random walks between two graphs are found by combining different weighted exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging.
Keywords: Product graph; Dual edge graph; Subgraph matching; Random walks; Graph kernel
|
|
|
Mohamed Ali Souibgui and 8 others. 2023. Text-DIAE: a self-supervised degradation invariant autoencoder for text recognition and document enhancement. Proceedings of the 37th AAAI Conference on Artificial Intelligence.
Abstract: In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantly, the proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time requiring substantially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at https://github.com/dali92002/SSL-OCR
Keywords: Representation Learning for Vision; CV Applications; CV Language and Vision; ML Unsupervised; Self-Supervised Learning
|
|
|
Lluis Gomez, Marçal Rusiñol and Dimosthenis Karatzas. 2018. Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters. 13th IAPR International Workshop on Document Analysis Systems.97–102.
Abstract: In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur.
Keywords: Robust Reading; End-to-end Systems; CNN; Utility Meters
|
|
|
Josep Llados, Horst Bunke and Enric Marti. 1997. Finding rotational symmetries by cyclic string matching. PRL, 18(14), 1435–1442.
Abstract: Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm
Keywords: Rotational symmetry; Reflectional symmetry; String matching
|
|
|
Josep Llados, Horst Bunke and Enric Marti. 1996. Structural Recognition of hand drawn floor plans. VI National Symposium on Pattern Recognition and Image Analysis. Cordoba.
Abstract: A system to recognize hand drawn architectural drawings in a CAD environment has been deve- loped. In this paper we focus on its high level interpretation module. To interpret a floor plan, the system must identify several building elements, whose description is stored in a library of pat- terns, as well as their spatial relationships. We propose a structural approach based on subgraph isomorphism techniques to obtain a high-level interpretation of the document. The vectorized input document and the patterns to be recognized are represented by attributed graphs. Discrete relaxation techniques (AC4 algorithm) have been applied to develop the matching algorithm. The process has been divided in three steps: node labeling, local consistency and global consistency verification. The hand drawn creation causes disturbed line drawings with several accuracy errors, which must be taken into account. Here we have identified them and the AC4 algorithm has been adapted to manage them.
Keywords: Rotational Symmetry; Reflectional Symmetry; String Matching.
|
|
|
Sangheeta Roy and 6 others. 2018. Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video. PR, 80, 64–82.
Abstract: Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization.
Keywords: Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition
|
|