2012 |
|
|
Muhammad Muzzamil Luqman, Jean-Yves Ramel and Josep Llados. 2012. Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop. Springer Berlin Heidelberg, 243–253. (LNCS.)
Abstract: Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named “fuzzy multilevel graph embedding – FMGE”, through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE.
|
|
|
|
Muhammad Muzzamil Luqman, Thierry Brouard, Jean-Yves Ramel and Josep Llados. 2012. Recherche de sous-graphes par encapsulation floue des cliques d'ordre 2: Application à la localisation de contenu dans les images de documents graphiques. Colloque International Francophone sur l'Écrit et le Document.149–162.
|
|
|
|
Nuria Cirera. 2012. Recognition of Handwritten Historical Documents. (Master's thesis, .)
|
|
|
|
Partha Pratim Roy, Umapada Pal and Josep Llados. 2012. Text line extraction in graphical documents using background and foreground. IJDAR, 15(3), 227–241.
Abstract: 0,405 JCR
In graphical documents (e.g., maps, engineering drawings), artistic documents etc., the text lines are annotated in multiple orientations or curvilinear way to illustrate different locations or symbols. For the optical character recognition of such documents, individual text lines from the documents need to be extracted. In this paper, we propose a novel method to segment such text lines and the method is based on the foreground and background information of the text components. To effectively utilize the background information, a water reservoir concept is used here. In the proposed scheme, at first, individual components are detected and grouped into character clusters in a hierarchical way using size and positional information. Next, the clusters are extended in two extreme sides to determine potential candidate regions. Finally, with the help of these candidate regions,
individual lines are extracted. The experimental results are presented on different datasets of graphical documents, camera-based warped documents, noisy images containing seals, etc. The results demonstrate that our approach is robust and invariant to size and orientation of the text lines present in
the document.
|
|
|
|
Partha Pratim Roy, Umapada Pal, Josep Llados and Mathieu Nicolas Delalandre. 2012. Multi-oriented touching text character segmentation in graphical documents using dynamic programming. PR, 45(5), 1972–1983.
Abstract: 2,292 JCR
The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes.
|
|
|
|
Sophie Wuerger, Kaida Xiao, Dimitris Mylonas, Q. Huang, Dimosthenis Karatzas and Galina Paramei. 2012. Blue green color categorization in mandarin english speakers. JOSA A, 29(2), A102–A1207.
Abstract: Observers are faster to detect a target among a set of distracters if the targets and distracters come from different color categories. This cross-boundary advantage seems to be limited to the right visual field, which is consistent with the dominance of the left hemisphere for language processing [Gilbert et al., Proc. Natl. Acad. Sci. USA 103, 489 (2006)]. Here we study whether a similar visual field advantage is found in the color identification task in speakers of Mandarin, a language that uses a logographic system. Forty late Mandarin-English bilinguals performed a blue-green color categorization task, in a blocked design, in their first language (L1: Mandarin) or second language (L2: English). Eleven color singletons ranging from blue to green were presented for 160 ms, randomly in the left visual field (LVF) or right visual field (RVF). Color boundary and reaction times (RTs) at the color boundary were estimated in L1 and L2, for both visual fields. We found that the color boundary did not differ between the languages; RTs at the color boundary, however, were on average more than 100 ms shorter in the English compared to the Mandarin sessions, but only when the stimuli were presented in the RVF. The finding may be explained by the script nature of the two languages: Mandarin logographic characters are analyzed visuospatially in the right hemisphere, which conceivably facilitates identification of color presented to the LVF.
|
|
|
|
Thanh Ha Do, Salvatore Tabbone and Oriol Ramos Terrades. 2012. Noise suppression over bi-level graphical documents using a sparse representation. Colloque International Francophone sur l'Écrit et le Document.
|
|
|
|
Thanh Ha Do, Salvatore Tabbone and Oriol Ramos Terrades. 2012. Text/graphic separation using a sparse representation with multi-learned dictionaries. 21st International Conference on Pattern Recognition.
Abstract: In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds.
Keywords: Graphics Recognition; Layout Analysis; Document Understandin
|
|
|
|
Volkmar Frinken, Alicia Fornes, Josep Llados and Jean-Marc Ogier. 2012. Bidirectional Language Model for Handwriting Recognition. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop. Springer Berlin Heidelberg, 611–619. (LNCS.)
Abstract: In order to improve the results of automatically recognized handwritten text, information about the language is commonly included in the recognition process. A common approach is to represent a text line as a sequence. It is processed in one direction and the language information via n-grams is directly included in the decoding. This approach, however, only uses context on one side to estimate a word’s probability. Therefore, we propose a bidirectional recognition in this paper, using distinct forward and a backward language models. By combining decoding hypotheses from both directions, we achieve a significant increase in recognition accuracy for the off-line writer independent handwriting recognition task. Both language models are of the same type and can be estimated on the same corpus. Hence, the increase in recognition accuracy comes without any additional need for training data or language modeling complexity.
|
|
|
|
Volkmar Frinken, Francisco Zamora, Salvador España, Maria Jose Castro, Andreas Fischer and Horst Bunke. 2012. Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition. 21st International Conference on Pattern Recognition.701–704.
Abstract: Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models.
|
|
