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Author |
Jialuo Chen; M.A.Souibgui; Alicia Fornes; Beata Megyesi |
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Title |
A Web-based Interactive Transcription Tool for Encrypted Manuscripts |
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Conference Article |
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Year |
2020 |
Publication |
3rd International Conference on Historical Cryptology |
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52-59 |
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Manual transcription of handwritten text is a time consuming task. In the case of encrypted manuscripts, the recognition is even more complex due to the huge variety of alphabets and symbol sets. To speed up and ease this process, we present a web-based tool aimed to (semi)-automatically transcribe the encrypted sources. The user uploads one or several images of the desired encrypted document(s) as input, and the system returns the transcription(s). This process is carried out in an interactive fashion with
the user to obtain more accurate results. For discovering and testing, the developed web tool is freely available. |
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Virtual; June 2020 |
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HistoCrypt |
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DAG; 600.140; 602.230; 600.121 |
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no |
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Call Number |
Admin @ si @ CSF2020 |
Serial |
3447 |
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Author |
Veronica Romero; Emilio Granell; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
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Title |
Information Extraction in Handwritten Marriage Licenses Books |
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Conference Article |
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Year |
2019 |
Publication |
5th International Workshop on Historical Document Imaging and Processing |
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66-71 |
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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. |
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Sydney; Australia; September 2019 |
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HIP |
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DAG; 600.140; 600.121 |
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no |
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Admin @ si @ RGF2019 |
Serial |
3352 |
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Author |
Manuel Carbonell; Joan Mas; Mauricio Villegas; Alicia Fornes; Josep Llados |
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Title |
End-to-End Handwritten Text Detection and Transcription in Full Pages |
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Conference Article |
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Year |
2019 |
Publication |
2nd International Workshop on Machine Learning |
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5 |
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29-34 |
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Keywords |
Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning |
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When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately. |
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Sydney; Australia; September 2019 |
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ICDAR WML |
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DAG; 600.140; 601.311; 600.140 |
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Call Number |
Admin @ si @ CMV2019 |
Serial |
3353 |
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Author |
Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados |
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Title |
Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning |
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Conference Article |
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Year |
2019 |
Publication |
13th IAPR International Workshop on Graphics Recognition |
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80-85 |
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Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning |
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With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results. |
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Sydney; Australia; September 2019 |
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GREC |
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DAG; 600.140; 601.302; 600.121 |
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no |
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Call Number |
Admin @ si @ BRF2019 |
Serial |
3354 |
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Author |
Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados |
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Title |
Table Detection in Invoice Documents by Graph Neural Networks |
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Conference Article |
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Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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122-127 |
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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. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.140; 601.302; 602.167; 600.121; 600.141 |
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Call Number |
Admin @ si @ RDG2019 |
Serial |
3355 |
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Author |
Ekta Vats; Anders Hast; Alicia Fornes |
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Title |
Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion |
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Conference Article |
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Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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1294-1299 |
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Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching |
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Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method. |
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Sydney; Australia; September 2019 |
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DAG; 600.140; 600.121 |
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Admin @ si @ VHF2019 |
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3356 |
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Author |
Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit |
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Title |
Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding |
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Journal Article |
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2021 |
Publication |
Pattern Recognition Letters |
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PRL |
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149 |
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164-171 |
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Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. |
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DAG; 600.121 |
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Admin @ si @ DGV2021 |
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3364 |
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Author |
Mohammed Al Rawi; Ernest Valveny |
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Title |
Compact and Efficient Multitask Learning in Vision, Language and Speech |
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2019 |
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IEEE International Conference on Computer Vision Workshops |
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2933-2942 |
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Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains. |
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Seul; Korea; October 2019 |
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ICCVW |
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DAG; 600.121; 600.129 |
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Admin @ si @ RaV2019 |
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3365 |
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Author |
Juan Ignacio Toledo |
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Title |
Information Extraction from Heterogeneous Handwritten Documents |
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2019 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In this thesis we explore information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes roughly equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words.
In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction. |
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July 2019 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Alicia Fornes;Josep Llados |
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978-84-948531-7-3 |
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DAG; 600.140; 600.121 |
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Admin @ si @ Tol2019 |
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3389 |
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Author |
Albert Berenguel |
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Title |
Analysis of background textures in banknotes and identity documents for counterfeit detection |
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2019 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Counterfeiting and piracy are a form of theft that has been steadily growing in recent years. A counterfeit is an unauthorized reproduction of an authentic/genuine object. Banknotes and identity documents are two common objects of counterfeiting. The former is used by organized criminal groups to finance a variety of illegal activities or even to destabilize entire countries due the inflation effect. Generally, in order to run their illicit businesses, counterfeiters establish companies and bank accounts using fraudulent identity documents. The illegal activities generated by counterfeit banknotes and identity documents has a damaging effect on business, the economy and the general population. To fight against counterfeiters, governments and authorities around the globe cooperate and develop security features to protect their security documents. Many of the security features in identity documents can also be found in banknotes. In this dissertation we focus our efforts in detecting the counterfeit banknotes and identity documents by analyzing the security features at the background printing. Background areas on secure documents contain fine-line patterns and designs that are difficult to reproduce without the manufacturers cutting-edge printing equipment. Our objective is to find the loose of resolution between the genuine security document and the printed counterfeit version with a publicly available commercial printer. We first present the most complete survey to date in identity and banknote security features. The compared algorithms and systems are based on computer vision and machine learning. Then we advance to present the banknote and identity counterfeit dataset we have built and use along all this thesis. Afterwards, we evaluate and adapt algorithms in the literature for the security background texture analysis. We study this problem from the point of view of robustness, computational efficiency and applicability into a real and non-controlled industrial scenario, proposing key insights to use these algorithms. Next, within the industrial environment of this thesis, we build a complete service oriented architecture to detect counterfeit documents. The mobile application and the server framework intends to be used even by non-expert document examiners to spot counterfeits. Later, we re-frame the problem of background texture counterfeit detection as a full-reference game of spotting the differences, by alternating glimpses between a counterfeit and a genuine background using recurrent neural networks. Finally, we deal with the lack of counterfeit samples, studying different approaches based on anomaly detection. |
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November 2019 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Oriol Ramos Terrades;Josep Llados |
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978-84-121011-2-6 |
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DAG; 600.140; 600.121 |
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Admin @ si @ Ber2019 |
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3395 |
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