Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2016). Sparse representation over learned dictionary for symbol recognition. SP - Signal Processing, 125, 36–47.
Abstract: In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.
Keywords: Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2016). Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary. In Recent Trends in Image Processing and Pattern Recognition (Vol. 709).
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2014). Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors. In 11th IAPR International Workshop on Document Analysis and Systems (pp. 156–160).
Abstract: This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising.
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2013). New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation. In 12th International Conference on Document Analysis and Recognition (pp. 265–269).
Abstract: In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learneddictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. Evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods.
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2013). Document noise removal using sparse representations over learned dictionary. In Symposium on Document engineering (pp. 161–168).
Abstract: best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental
results on several datasets demonstrate the robustness of our method compared with the state-of-the-art.
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2012). Text/graphic separation using a sparse representation with multi-learned dictionaries. In 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
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2012). Noise suppression over bi-level graphical documents using a sparse representation. In Colloque International Francophone sur l'Écrit et le Document.
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