| |
Records |
Links |
|
Author |
Nibal Nayef; Yash Patel; Michal Busta; Pinaki Nath Chowdhury; Dimosthenis Karatzas; Wafa Khlif; Jiri Matas; Umapada Pal; Jean-Christophe Burie; Cheng-lin Liu; Jean-Marc Ogier |
|
| |
Title |
ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019 |
Type |
Conference Article |
| |
Year  |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1582-1587 |
|
| |
Keywords |
|
|
| |
Abstract |
With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge. |
|
| |
Address |
Sydney; Australia; September 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICDAR |
|
| |
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ NPB2019 |
Serial |
3341 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados |
|
| |
Title |
Table Detection in Invoice Documents by Graph Neural Networks |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
122-127 |
|
| |
Keywords |
|
|
| |
Abstract |
Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text
reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity
of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research. |
|
| |
Address |
Sydney; Australia; September 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICDAR |
|
| |
Notes |
DAG; 600.140; 601.302; 602.167; 600.121; 600.141 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RDG2019 |
Serial |
3355 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas |
|
| |
Title |
Selective Style Transfer for Text |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
805-812 |
|
| |
Keywords |
transfer; text style transfer; data augmentation; scene text detection |
|
| |
Abstract |
This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means
transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available. |
|
| |
Address |
Sydney; Australia; September 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICDAR |
|
| |
Notes |
DAG; 600.129; 600.135; 601.338; 601.310; 600.121 |
Approved |
no |
|
| |
Call Number |
GBG2019 |
Serial |
3265 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
|
| |
Title |
Self-Supervised Learning from Web Data for Multimodal Retrieval |
Type |
Book Chapter |
| |
Year |
2019 |
Publication |
Multi-Modal Scene Understanding Book |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
279-306 |
|
| |
Keywords |
self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding |
|
| |
Abstract |
Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learnt joint image and text embeddingspace. Weperformathoroughanalysisandperformancecomparisonoffivedifferentstateof the art text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text basedimageretrievaltask,andweclearlyoutperformstateoftheartintheMIRFlickrdatasetwhen training in the target data. Further, we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.129; 601.338; 601.310 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GGG2019 |
Serial |
3266 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Rui Zhang; Yongsheng Zhou; Qianyi Jiang; Qi Song; Nan Li; Kai Zhou; Lei Wang; Dong Wang; Minghui Liao; Mingkun Yang; Xiang Bai; Baoguang Shi; Dimosthenis Karatzas; Shijian Lu; CV Jawahar |
|
| |
Title |
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1577-1581 |
|
| |
Keywords |
|
|
| |
Abstract |
Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinesecharacters can be arranged in various layouts with numerous fonts. The Chinese signboards in street view are a good choice for Chinese scene text images since they have different backgrounds, fonts and layouts. We organized a competition called ICDAR2019-ReCTS, which mainly focuses on reading Chinese text on signboard. This report presents the final results of the competition. A large-scale dataset of 25,000 annotated signboard images, in which all the text lines and characters are annotated with locations and transcriptions, were released. Four tasks, namely character recognition, text line recognition, text line detection and end-to-end recognition were set up. Besides, considering the Chinese text ambiguity issue, we proposed a multi ground truth (multi-GT) evaluation method to make evaluation fairer. The competition started on March 1, 2019 and ended on April 30, 2019. 262 submissions from 46 teams are received. Most of the participants come from universities, research institutes, and tech companies in China. There are also some participants from the United States, Australia, Singapore, and Korea. 21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4. |
|
| |
Address |
Sydney; Australia; September 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICDAR |
|
| |
Notes |
DAG; 600.129; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ LZZ2019 |
Serial |
3335 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |
|
| |
Title |
Evaluation of the Effect of Improper Segmentation on Word Spotting |
Type |
Journal Article |
| |
Year |
2019 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
| |
Volume |
22 |
Issue |
|
Pages |
361-374 |
|
| |
Keywords |
|
|
| |
Abstract |
Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.097; 600.084; 600.121; 600.140; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DNL2019 |
Serial |
3455 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sounak Dey; Pau Riba; Anjan Dutta; Josep Llados; Yi-Zhe Song |
|
| |
Title |
Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2179-2188 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future research. |
|
| |
Address |
Long beach; CA; USA; June 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPR |
|
| |
Notes |
DAG; 600.140; 600.121; 600.097 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DRD2019 |
Serial |
3462 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone |
|
| |
Title |
DSD: document sparse-based denoising algorithm |
Type |
Journal Article |
| |
Year |
2019 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
|
| |
Volume |
22 |
Issue |
1 |
Pages |
177–186 |
|
| |
Keywords |
Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models |
|
| |
Abstract |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.097; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DRT2019 |
Serial |
3254 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Veronica Romero; Emilio Granell; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
|
| |
Title |
Information Extraction in Handwritten Marriage Licenses Books |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
5th International Workshop on Historical Document Imaging and Processing |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
66-71 |
|
| |
Keywords |
|
|
| |
Abstract |
Handwritten marriage licenses books are characterized by a simple structure of the text in the records with an evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. Previous works have shown that the use of category-based language models and a Grammatical Inference technique known as MGGI can improve the accuracy of these
tasks. However, the application of the MGGI algorithm requires an a priori knowledge to label the words of the training strings, that is not always easy to obtain. In this paper we study how to automatically obtain the information required by the MGGI algorithm using a technique based on Confusion Networks. Using the resulting language model, full handwritten text recognition and information extraction experiments have been carried out with results supporting the proposed approach. |
|
| |
Address |
Sydney; Australia; September 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
HIP |
|
| |
Notes |
DAG; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RGF2019 |
Serial |
3352 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
|
| |
Title |
Self-Supervised Visual Representations for Cross-Modal Retrieval |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
ACM International Conference on Multimedia Retrieval |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
182–186 |
|
| |
Keywords |
|
|
| |
Abstract |
Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset. |
|
| |
Address |
Otawa; Canada; june 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICMR |
|
| |
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PGR2019 |
Serial |
3288 |
|
| Permanent link to this record |
