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Author |
Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
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Title |
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images |
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Conference Article |
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2020 |
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16th European Conference on Computer Vision |
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Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.140; 600.121; 600.129 |
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Admin @ si @ KPW2020 |
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3426 |
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Author |
Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor |
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Title |
A Few-shot Learning Approach for Historical Encoded Manuscript Recognition |
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2021 |
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25th International Conference on Pattern Recognition |
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5413-5420 |
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Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition. |
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Virtual; January 2021 |
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ICPR |
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DAG; 600.121; 600.140 |
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Admin @ si @ SFK2021 |
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3449 |
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Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes |
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A conditional GAN based approach for distorted camera captured documents recovery |
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2020 |
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4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence |
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Virtual; December 2020 |
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MedPRAI |
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DAG; 600.121 |
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Admin @ si @ SKF2020 |
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3450 |
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Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados |
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Title |
A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages |
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2020 |
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Pattern Recognition Letters |
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PRL |
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136 |
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219-227 |
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In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks. |
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DAG; 600.140; 601.311; 600.121 |
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Admin @ si @ CFV2020 |
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3451 |
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B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols |
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Title |
Knowledge graph based methods for record linkage |
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Journal Article |
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2020 |
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Pattern Recognition Letters |
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PRL |
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136 |
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127-133 |
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Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.
In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results. |
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DAG; 600.140; 600.121 |
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Admin @ si @ GRP2020 |
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3453 |
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Author |
Mohamed Ali Souibgui; Y.Kessentini |
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Title |
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement |
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Journal Article |
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2022 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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44 |
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3 |
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1180-1191 |
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Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems. |
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1 March 2022 |
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DAG; 602.230; 600.121; 600.140 |
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Admin @ si @ SoK2022 |
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3454 |
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Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |
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Title |
Evaluation of the Effect of Improper Segmentation on Word Spotting |
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2019 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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22 |
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361-374 |
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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. |
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DAG; 600.097; 600.084; 600.121; 600.140; 600.129 |
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Admin @ si @ DNL2019 |
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3455 |
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Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
Recurrent Comparator with attention models to detect counterfeit documents |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms state-of-the-art classification approaches for counterfeit detection as demonstrated in the evaluation. |
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Sidney; Australia; September 2019 |
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ICDAR |
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DAG; 600.140; 600.121; 601.269 |
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Admin @ si @ BRL2019 |
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3456 |
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Author |
Fernando Vilariño |
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Title |
Library Living Lab, Numérisation 3D des chapiteaux du cloître de Saint-Cugat : des citoyens co- créant le nouveau patrimoine culturel numérique |
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2019 |
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Intersectorialité et approche Living Labs. Entretiens Jacques-Cartier |
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Montreal; Canada; December 2019 |
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MV; DAG; 600.140; 600.121;SIAI |
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Admin @ si @ Vil2019a |
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3457 |
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Fernando Vilariño |
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Public Libraries Exploring how technology transforms the cultural experience of people |
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2019 |
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Workshop on Social Impact of AI. Open Living Lab Days Conference. |
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Thessaloniki; Grecia; September 2019 |
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MV; DAG; 600.140; 600.121;SIAI |
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Admin @ si @ Vil2019b |
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3458 |
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