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Author  |
Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone |
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Title |
DSD: document sparse-based denoising algorithm |
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Journal Article |
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Year |
2019 |
Publication |
Pattern Analysis and Applications |
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PAA |
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22 |
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1 |
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177–186 |
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Keywords |
Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models |
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In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising. |
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DAG; 600.097; 600.140; 600.121 |
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Admin @ si @ DRT2019 |
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3254 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation |
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Conference Article |
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Year |
2013 |
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12th International Conference on Document Analysis and Recognition |
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265-269 |
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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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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ DTR2013b |
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2331 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Document noise removal using sparse representations over learned dictionary |
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Conference Article |
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Year |
2013 |
Publication |
Symposium on Document engineering |
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161-168 |
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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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Barcelona; October 2013 |
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978-1-4503-1789-4 |
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ACM-DocEng |
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DAG; 600.061 |
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no |
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Admin @ si @ DTR2013a |
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2330 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Text/graphic separation using a sparse representation with multi-learned dictionaries |
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Conference Article |
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2012 |
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21st International Conference on Pattern Recognition |
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Graphics Recognition; Layout Analysis; Document Understandin |
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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. |
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Tsukuba |
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ICPR |
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DAG |
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no |
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Admin @ si @ DTR2012a |
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2135 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Noise suppression over bi-level graphical documents using a sparse representation |
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Conference Article |
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2012 |
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Colloque International Francophone sur l'Écrit et le Document |
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Bordeaux |
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CIFED |
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DAG |
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no |
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Call Number |
Admin @ si @ DTR2012b |
Serial |
2136 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors |
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Conference Article |
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Year |
2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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156-160 |
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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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978-1-4799-3243-6 |
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DAS |
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Notes |
DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ DTR2014 |
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2543 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Sparse representation over learned dictionary for symbol recognition |
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Journal Article |
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Year |
2016 |
Publication |
Signal Processing |
Abbreviated Journal |
SP |
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125 |
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36-47 |
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Keywords |
Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
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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. |
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DAG; 600.061; 600.077 |
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Admin @ si @ DTR2016 |
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2946 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary |
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Book Chapter |
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Year |
2016 |
Publication |
Recent Trends in Image Processing and Pattern Recognition |
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709 |
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RTIP2R |
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DAG |
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Admin @ si @ HTR2016 |
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2956 |
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Author |
Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam |
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Title |
Subgraph spotting in graph representations of comic book images |
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Journal Article |
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2018 |
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Pattern Recognition Letters |
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PRL |
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112 |
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118-124 |
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Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images |
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Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. |
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DAG; 600.097; 600.121 |
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Admin @ si @ LLD2018 |
Serial |
3150 |
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Author |
Umapada Pal; Partha Pratim Roy; N. Tripathya; Josep Llados |
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Title |
Multi-oriented Bangla and Devnagari text recognition |
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Journal Article |
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2010 |
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Pattern Recognition |
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PR |
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43 |
Issue |
12 |
Pages |
4124–4136 |
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Abstract |
There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters.
For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters. |
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Elsevier |
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DAG |
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DAG @ dag @ PRT2010 |
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1337 |
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