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Author Joana Maria Pujadas-Mora; Alicia Fornes; Oriol Ramos Terrades; Josep Llados; Jialuo Chen; Miquel Valls-Figols; Anna Cabre edit  doi
openurl 
  Title The Barcelona Historical Marriage Database and the Baix Llobregat Demographic Database. From Algorithms for Handwriting Recognition to Individual-Level Demographic and Socioeconomic Data Type Journal
  Year (down) 2022 Publication Historical Life Course Studies Abbreviated Journal HLCS  
  Volume 12 Issue Pages 99-132  
  Keywords Individual demographic databases; Computer vision, Record linkage; Social mobility; Inequality; Migration; Word spotting; Handwriting recognition; Local censuses; Marriage Licences  
  Abstract The Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451–1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography.  
  Address June 23, 2022  
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  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ Serial 3737  
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Author Josep Brugues Pujolras; Lluis Gomez; Dimosthenis Karatzas edit  doi
openurl 
  Title A Multilingual Approach to Scene Text Visual Question Answering Type Conference Article
  Year (down) 2022 Publication Document Analysis Systems.15th IAPR International Workshop, (DAS2022) Abbreviated Journal  
  Volume Issue Pages 65-79  
  Keywords Scene text; Visual question answering; Multilingual word embeddings; Vision and language; Deep learning  
  Abstract Scene Text Visual Question Answering (ST-VQA) has recently emerged as a hot research topic in Computer Vision. Current ST-VQA models have a big potential for many types of applications but lack the ability to perform well on more than one language at a time due to the lack of multilingual data, as well as the use of monolingual word embeddings for training. In this work, we explore the possibility to obtain bilingual and multilingual VQA models. In that regard, we use an already established VQA model that uses monolingual word embeddings as part of its pipeline and substitute them by FastText and BPEmb multilingual word embeddings that have been aligned to English. Our experiments demonstrate that it is possible to obtain bilingual and multilingual VQA models with a minimal loss in performance in languages not used during training, as well as a multilingual model trained in multiple languages that match the performance of the respective monolingual baselines.  
  Address La Rochelle, France; May 22–25, 2022  
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  Area Expedition Conference DAS  
  Notes DAG; 611.004; 600.155; 601.002 Approved no  
  Call Number Admin @ si @ Serial 3695  
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Author Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados edit  doi
openurl 
  Title Classification of aesthetic natural scene images using statistical and semantic features Type Journal Article
  Year (down) 2022 Publication Multimedia Tools and Applications Abbreviated Journal  
  Volume Issue Pages MTAP  
  Keywords  
  Abstract Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images.  
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  Notes DAG; 600.121; 600.162; Approved no  
  Call Number Admin @ si @ Serial 3742  
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Author Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas edit   file
url  doi
openurl 
  Title Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition Type Journal Article
  Year (down) 2022 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 129 Issue Pages 108766  
  Keywords  
  Abstract The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios.  
  Address Sept. 2022  
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  Notes DAG; 600.121; 600.162 Approved no  
  Call Number Admin @ si @ KRR2020b Serial 3556  
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Author Minesh Mathew; Viraj Bagal; Ruben Tito; Dimosthenis Karatzas; Ernest Valveny; C.V. Jawahar edit   pdf
openurl 
  Title InfographicVQA Type Conference Article
  Year (down) 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1697-1706  
  Keywords  
  Abstract Infographics communicate information using a combination of textual, graphical and visual elements. This work explores the automatic understanding of infographic images by using a Visual Question Answering technique. To this end, we present InfographicVQA, a new dataset comprising a diverse collection of infographics and question-answer annotations. The questions require methods that jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with an emphasis on questions that require elementary reasoning and basic arithmetic skills. For VQA on the dataset, we evaluate two Transformer-based strong baselines. Both the baselines yield unsatisfactory results compared to near perfect human performance on the dataset. The results suggest that VQA on infographics--images that are designed to communicate information quickly and clearly to human brain--is ideal for benchmarking machine understanding of complex document images. The dataset is available for download at docvqa. org  
  Address Virtual; Waikoloa; Hawai; USA; January 2022  
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  Area Expedition Conference WACV  
  Notes DAG; 600.155 Approved no  
  Call Number MBT2022 Serial 3625  
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Author Mohamed Ali Souibgui; Y.Kessentini edit   pdf
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  Title DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement Type Journal Article
  Year (down) 2022 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 44 Issue 3 Pages 1180-1191  
  Keywords  
  Abstract 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.  
  Address 1 March 2022  
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  Notes DAG; 602.230; 600.121; 600.140 Approved no  
  Call Number Admin @ si @ SoK2020 Serial 3454  
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Author Mohamed Ali Souibgui; Ali Furkan Biten; Sounak Dey; Alicia Fornes; Yousri Kessentini; Lluis Gomez; Dimosthenis Karatzas; Josep Llados edit   pdf
openurl 
  Title One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition Type Conference Article
  Year (down) 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). This appears, for example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the content. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method, achieving competitive results compared to the usage of real annotated data.  
  Address Virtual; January 2022  
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  Area Expedition Conference WACV  
  Notes DAG; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ SBD2022 Serial 3615  
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Author Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi edit  doi
openurl 
  Title Few shots are all you need: A progressive learning approach for low resource handwritten text recognition Type Journal Article
  Year (down) 2022 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 160 Issue Pages 43-49  
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  Abstract Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
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  Notes DAG; 600.121; 600.162; 602.230 Approved no  
  Call Number Admin @ si @ Serial 3736  
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Author Mohamed Ali Souibgui; Asma Bensalah; Jialuo Chen; Alicia Fornes; Michelle Waldispühl edit  doi
openurl 
  Title A User Perspective on HTR methods for the Automatic Transcription of Rare Scripts: The Case of Codex Runicus Just Accepted Type Journal Article
  Year (down) 2022 Publication ACM Journal on Computing and Cultural Heritage Abbreviated Journal JOCCH  
  Volume Issue Pages  
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  Abstract Recent breakthroughs in Artificial Intelligence, Deep Learning and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the transcription of historical documents. However, for documents in rare scripts with few labelled training data available, current Handwritten Text Recognition (HTR) systems are too constraint. Moreover, research on HTR often focuses on technical aspects only, and rarely puts emphasis on implementing software tools for scholars in Humanities. In this article, we describe, compare and analyse different transcription methods for rare scripts. We evaluate their performance in a real use case of a medieval manuscript written in the runic script (Codex Runicus) and discuss advantages and disadvantages of each method from the user perspective. From this exhaustive analysis and comparison with a fully manual transcription, we raise conclusions and provide recommendations to scholars interested in using automatic transcription tools.  
  Address July 2022  
  Corporate Author Thesis  
  Publisher ACM Place of Publication Editor  
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  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ Serial 3732  
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Author Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal edit  doi
openurl 
  Title DocEnTr: An End-to-End Document Image Enhancement Transformer Type Conference Article
  Year (down) 2022 Publication 26th International Conference on Pattern Recognition (ICPR) Abbreviated Journal  
  Volume Issue Pages 1699-1705  
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  Abstract Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR  
  Address August 21-25, 2022 , Montréal Québec  
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  Area Expedition Conference ICPR  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ Serial 3730  
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