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| Author | Masakazu Iwamura; Naoyuki Morimoto; Keishi Tainaka; Dena Bazazian; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | ICDAR2017 Robust Reading Challenge on Omnidirectional Video | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | Results of ICDAR 2017 Robust Reading Challenge on Omnidirectional Video are presented. This competition uses Downtown Osaka Scene Text (DOST) Dataset that was captured in Osaka, Japan with an omnidirectional camera. Hence, it consists of sequential images (videos) of different view angles. Regarding the sequential images as videos (video mode), two tasks of localisation and end-to-end recognition are prepared. Regarding them as a set of still images (still image mode), three tasks of localisation, cropped word recognition and end-to-end recognition are prepared. As the dataset has been captured in Japan, the dataset contains Japanese text but also include text consisting of alphanumeric characters (Latin text). Hence, a submitted result for each task is evaluated in three ways: using Japanese only ground truth (GT), using Latin only GT and using combined GTs of both. Finally, by the submission deadline, we have received two submissions in the text localisation task of the still image mode. We intend to continue the competition in the open mode. Expecting further submissions, in this report we provide baseline results in all the tasks in addition to the submissions from the community. | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ IMT2017 | Serial | 3077 | ||
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| Author | ChunYang; Xu Cheng Yin; Hong Yu; Dimosthenis Karatzas; Yu Cao | ||||
| Title | ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT) | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1444-1447 | ||
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| Abstract | Hundreds of millions of figures are available in the biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information and understanding biomedical documents. Unlike images in the open domain, biomedical figures present a variety of unique challenges. For example, biomedical figures typically have complex layouts, small font sizes, short text, specific text, complex symbols and irregular text arrangements. This paper presents the final results of the ICDAR 2017 Competition on Text Extraction from Biomedical Literature Figures (ICDAR2017 DeTEXT Competition), which aims at extracting (detecting and recognizing) text from biomedical literature figures. Similar to text extraction from scene images and web pictures, ICDAR2017 DeTEXT Competition includes three major tasks, i.e., text detection, cropped word recognition and end-to-end text recognition. Here, we describe in detail the data set, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods. | ||||
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| ISSN | ISBN | 978-1-5386-3586-5 | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ YCY2017 | Serial | 3098 | ||
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| Author | David Aldavert; Marçal Rusiñol; Ricardo Toledo | ||||
| Title | Automatic Static/Variable Content Separation in Administrative Document Images | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ ART2017 | Serial | 3001 | ||
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| Author | Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero | ||||
| Title | Evaluation of Texture Descriptors for Validation of Counterfeit Documents | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1237-1242 | ||
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| Abstract | This paper describes an exhaustive comparative analysis and evaluation of different existing texture descriptor algorithms to differentiate between genuine and counterfeit documents. We include in our experiments different categories of algorithms and compare them in different scenarios with several counterfeit datasets, comprising banknotes and identity documents. Computational time in the extraction of each descriptor is important because the final objective is to use it in a real industrial scenario. HoG and CNN based descriptors stands out statistically over the rest in terms of the F1-score/time ratio performance. | ||||
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| ISSN | 2379-2140 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.061; 601.269; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRL2017 | Serial | 3092 | ||
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| Author | Suman Ghosh; Ernest Valveny | ||||
| Title | Visual attention models for scene text recognition | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | arXiv:1706.01487
In this paper we propose an approach to lexicon-free recognition of text in scene images. Our approach relies on a LSTM-based soft visual attention model learned from convolutional features. A set of feature vectors are derived from an intermediate convolutional layer corresponding to different areas of the image. This permits encoding of spatial information into the image representation. In this way, the framework is able to learn how to selectively focus on different parts of the image. At every time step the recognizer emits one character using a weighted combination of the convolutional feature vectors according to the learned attention model. Training can be done end-to-end using only word level annotations. In addition, we show that modifying the beam search algorithm by integrating an explicit language model leads to significantly better recognition results. We validate the performance of our approach on standard SVT and ICDAR'03 scene text datasets, showing state-of-the-art performance in unconstrained text recognition. |
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GhV2017b | Serial | 3080 | ||
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| Author | Suman Ghosh; Ernest Valveny | ||||
| Title | R-PHOC: Segmentation-Free Word Spotting using CNN | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Keywords | Convolutional neural network; Image segmentation; Artificial neural network; Nearest neighbor search | ||||
| Abstract | arXiv:1707.01294
This paper proposes a region based convolutional neural network for segmentation-free word spotting. Our network takes as input an image and a set of word candidate bound- ing boxes and embeds all bounding boxes into an embedding space, where word spotting can be casted as a simple nearest neighbour search between the query representation and each of the candidate bounding boxes. We make use of PHOC embedding as it has previously achieved significant success in segmentation- based word spotting. Word candidates are generated using a simple procedure based on grouping connected components using some spatial constraints. Experiments show that R-PHOC which operates on images directly can improve the current state-of- the-art in the standard GW dataset and performs as good as PHOCNET in some cases designed for segmentation based word spotting. |
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GhV2017a | Serial | 3079 | ||
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| Author | Suman Ghosh; Lluis Gomez; Dimosthenis Karatzas; Ernest Valveny | ||||
| Title | Efficient indexing for Query By String text retrieval | Type | Conference Article | ||
| Year | 2015 | Publication | 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 1236 - 1240 | ||
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| Abstract | This paper deals with Query By String word spotting in scene images. A hierarchical text segmentation algorithm based on text specific selective search is used to find text regions. These regions are indexed per character n-grams present in the text region. An attribute representation based on Pyramidal Histogram of Characters (PHOC) is used to compare text regions with the query text. For generation of the index a similar attribute space based Pyramidal Histogram of character n-grams is used. These attribute models are learned using linear SVMs over the Fisher Vector [1] representation of the images along with the PHOC labels of the corresponding strings. | ||||
| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | CBDAR | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GGK2015 | Serial | 2693 | ||
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| Author | J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier | ||||
| Title | Improving Document Matching Performance by Local Descriptor Filtering | Type | Conference Article | ||
| Year | 2015 | Publication | 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 1216 - 1220 | ||
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| Abstract | In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | CBDAR | ||
| Notes | DAG; 600.077; 601.223; 600.084 | Approved | no | ||
| Call Number | Admin @ si @ CRO2015a | Serial | 2680 | ||
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| Author | Dimosthenis Karatzas; Lluis Gomez; Anguelos Nicolaou; Suman Ghosh; Andrew Bagdanov; Masakazu Iwamura; J. Matas; L. Neumann; V. Ramaseshan; S. Lu ; Faisal Shafait; Seiichi Uchida; Ernest Valveny | ||||
| Title | ICDAR 2015 Competition on Robust Reading | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 1156-1160 | ||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077; 600.084 | Approved | no | ||
| Call Number | Admin @ si @ KGN2015 | Serial | 2690 | ||
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| Author | Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados; David Fernandez; Cristina Cañero | ||||
| Title | Use case visual Bag-of-Words techniques for camera based identity document classification | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 721 - 725 | ||
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| Abstract | Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes. | ||||
| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077; 600.061; | Approved | no | ||
| Call Number | Admin @ si @ HRL2015a | Serial | 2726 | ||
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| Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier; Josep Llados | ||||
| Title | A Comparative Study of Local Detectors and Descriptors for Mobile Document Classification | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 596-600 | ||
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| Abstract | In this paper we conduct a comparative study of local key-point detectors and local descriptors for the specific task of mobile document classification. A classification architecture based on direct matching of local descriptors is used as baseline for the comparative study. A set of four different key-point
detectors and four different local descriptors are tested in all the possible combinations. The experiments are conducted in a database consisting of 30 model documents acquired on 6 different backgrounds, totaling more than 36.000 test images. |
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| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.61; 601.223; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RCO2015 | Serial | 2684 | ||
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| Author | R. Bertrand; Oriol Ramos Terrades; P. Gomez-Kramer; P. Franco; Jean-Marc Ogier | ||||
| Title | A Conditional Random Field model for font forgery detection | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 576 - 580 | ||
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| Abstract | Nowadays, document forgery is becoming a real issue. A large amount of documents that contain critical information as payment slips, invoices or contracts, are constantly subject to fraudster manipulation because of the lack of security regarding this kind of document. Previously, a system to detect fraudulent documents based on its intrinsic features has been presented. It was especially designed to retrieve copy-move forgery and imperfection due to fraudster manipulation. However, when a set of characters is not present in the original document, copy-move forgery is not feasible. Hence, the fraudster will use a text toolbox to add or modify information in the document by imitating the font or he will cut and paste characters from another document where the font properties are similar. This often results in font type errors. Thus, a clue to detect document forgery consists of finding characters, words or sentences in a document with font properties different from their surroundings. To this end, we present in this paper an automatic forgery detection method based on document font features. Using the Conditional Random Field a measurement of probability that a character belongs to a specific font is made by comparing the character font features to a knowledge database. Then, the character is classified as a genuine or a fake one by comparing its probability to belong to a certain font type with those of the neighboring characters. | ||||
| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ BRG2015 | Serial | 2725 | ||
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| Author | Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados | ||||
| Title | Towards Query-by-Speech Handwritten Keyword Spotting | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 501-505 | ||
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| Abstract | In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset. |
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| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.061; 601.223; 600.077;ADAS | Approved | no | ||
| Call Number | Admin @ si @ RAT2015b | Serial | 2682 | ||
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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation | Type | Conference Article | ||
| Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 265-269 | ||
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| 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. | ||||
| Address | Washington; USA; August 2013 | ||||
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| ISSN | 1520-5363 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ DTR2013b | Serial | 2331 | ||
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| Author | Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados; Tomokazu Sato; Masakazu Iwamura; Koichi Kise | ||||
| Title | Key-region detection for document images -applications to administrative document retrieval | Type | Conference Article | ||
| Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 230-234 | ||
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| Abstract | In this paper we argue that a key-region detector designed to take into account the special characteristics of document images can result in the detection of less and more meaningful key-regions. We propose a fast key-region detector able to capture aspects of the structural information of the document, and demonstrate its efficiency by comparing against standard detectors in an administrative document retrieval scenario. We show that using the proposed detector results to a smaller number of detected key-regions and higher performance without any drop in speed compared to standard state of the art detectors. | ||||
| Address | Washington; USA; August 2013 | ||||
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| ISSN | 1520-5363 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.056; 600.045 | Approved | no | ||
| Call Number | Admin @ si @ GRK2013b | Serial | 2293 | ||
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