Andreas Fischer, Volkmar Frinken, Alicia Fornes, & Horst Bunke. (2011). Transcription Alignment of Latin Manuscripts Using Hidden Markov Models. In Proceedings of the 2011 Workshop on Historical Document Imaging and Processing (pp. 29–36). ACM.
Abstract: Transcriptions of historical documents are a valuable source for extracting labeled handwriting images that can be used for training recognition systems. In this paper, we introduce the Saint Gall database that includes images as well as the transcription of a Latin manuscript from the 9th century written in Carolingian script. Although the available transcription is of high quality for a human reader, the spelling of the words is not accurate when compared with the handwriting image. Hence, the transcription poses several challenges for alignment regarding, e.g., line breaks, abbreviations, and capitalization. We propose an alignment system based on character Hidden Markov Models that can cope with these challenges and efficiently aligns complete document pages. On the Saint Gall database, we demonstrate that a considerable alignment accuracy can be achieved, even with weakly trained character models.
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David Fernandez, Simone Marinai, Josep Llados, & Alicia Fornes. (2013). Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks. In 2nd International Workshop on Historical Document Imaging and Processing (pp. 36–43).
Abstract: Natural languages can often be modelled by suitable grammars whose knowledge can improve the word spotting results. The implicit contextual information is even more useful when dealing with information that is intrinsically described as one collection of records. In this paper, we present one approach to word spotting which uses the contextual information of records to improve the results. The method relies on Markov Logic Networks to probabilistically model the relational organization of handwritten records. The performance has been evaluated on the Barcelona Marriages Dataset that contains structured handwritten records that summarize marriage information.
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Volkmar Frinken, Andreas Fischer, & Carlos David Martinez Hinarejos. (2013). Handwriting Recognition in Historical Documents using Very Large Vocabularies. In 2nd International Workshop on Historical Document Imaging and Processing (pp. 67–72).
Abstract: Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words.
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Veronica Romero, Emilio Granell, Alicia Fornes, Enrique Vidal, & Joan Andreu Sanchez. (2019). Information Extraction in Handwritten Marriage Licenses Books. In 5th International Workshop on Historical Document Imaging and Processing (pp. 66–71).
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.
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Mohamed Ali Souibgui, Pau Torras, Jialuo Chen, & Alicia Fornes. (2023). An Evaluation of Handwritten Text Recognition Methods for Historical Ciphered Manuscripts. In 7th International Workshop on Historical Document Imaging and Processing (pp. 7–12).
Abstract: This paper investigates the effectiveness of different deep learning HTR families, including LSTM, Seq2Seq, and transformer-based approaches with self-supervised pretraining, in recognizing ciphered manuscripts from different historical periods and cultures. The goal is to identify the most suitable method or training techniques for recognizing ciphered manuscripts and to provide insights into the challenges and opportunities in this field of research. We evaluate the performance of these models on several datasets of ciphered manuscripts and discuss their results. This study contributes to the development of more accurate and efficient methods for recognizing historical manuscripts for the preservation and dissemination of our cultural heritage.
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Jialuo Chen, M.A.Souibgui, Alicia Fornes, & Beata Megyesi. (2020). A Web-based Interactive Transcription Tool for Encrypted Manuscripts. In 3rd International Conference on Historical Cryptology (pp. 52–59).
Abstract: 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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Jialuo Chen, Mohamed Ali Souibgui, Alicia Fornes, & Beata Megyesi. (2021). Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images. In 4th International Conference on Historical Cryptology (pp. 34–37).
Abstract: Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering.
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Sergio Escalera, Jordi Gonzalez, Xavier Baro, Fernando Alonso, & Martha Mackay. (2016). Care Respite: a remote monitoring eHealth system for improving ambient assisted living. In Human Motion Analysis for Healthcare Applications.
Abstract: Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.
In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions.
This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations.
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Laura Igual, Joan Carles Soliva, Antonio Hernandez, Sergio Escalera, Oscar Vilarroya, & Petia Radeva. (2012). Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder. In High Performance Computing and Simulation, International Conference on (pp. 182–187). IEEE Xplore.
Abstract: This paper presents an automatic method for external and internal segmentation of the caudate nucleus in Magnetic Resonance Images (MRI) based on statistical and structural machine learning approaches. This method is applied in Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The external segmentation method adapts the Graph Cut energy-minimization model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus. In particular, new energy function data and boundary potentials are defined and a supervised energy term based on contextual brain structures is added. Furthermore, the internal segmentation method learns a classifier based on shape features of the Region of Interest (ROI) in MRI slices. The results show accurate external and internal caudate segmentation in a real data set and similar performance of ADHD diagnostic test to manual annotation.
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Petia Radeva, Michal Drozdzal, Santiago Segui, Laura Igual, Carolina Malagelada, Fernando Azpiroz, et al. (2012). Active labeling: Application to wireless endoscopy analysis. In High Performance Computing and Simulation, International Conference on (pp. 174–181).
Abstract: Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”.
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Sandra Jimenez, Xavier Otazu, Valero Laparra, & Jesus Malo. (2013). Chromatic induction and contrast masking: similar models, different goals? In Human Vision and Electronic Imaging XVIII (Vol. 8651).
Abstract: Normalization of signals coming from linear sensors is an ubiquitous mechanism of neural adaptation.1 Local interaction between sensors tuned to a particular feature at certain spatial position and neighbor sensors explains a wide range of psychophysical facts including (1) masking of spatial patterns, (2) non-linearities of motion sensors, (3) adaptation of color perception, (4) brightness and chromatic induction, and (5) image quality assessment. Although the above models have formal and qualitative similarities, it does not necessarily mean that the mechanisms involved are pursuing the same statistical goal. For instance, in the case of chromatic mechanisms (disregarding spatial information), different parameters in the normalization give rise to optimal discrimination or adaptation, and different non-linearities may give rise to error minimization or component independence. In the case of spatial sensors (disregarding color information), a number of studies have pointed out the benefits of masking in statistical independence terms. However, such statistical analysis has not been performed for spatio-chromatic induction models where chromatic perception depends on spatial configuration. In this work we investigate whether successful spatio-chromatic induction models,6 increase component independence similarly as previously reported for masking models. Mutual information analysis suggests that seeking an efficient chromatic representation may explain the prevalence of induction effects in spatially simple images. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Giacomo Magnifico, Beata Megyesi, Mohamed Ali Souibgui, Jialuo Chen, & Alicia Fornes. (2022). Lost in Transcription of Graphic Signs in Ciphers. In International Conference on Historical Cryptology (HistoCrypt 2022) (pp. 153–158).
Abstract: Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings.
Keywords: transcription of ciphers; hand-written text recognition of symbols; graphic signs
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Rosa Maria Ortiz, Debora Gil, Elisa Minchole, Marta Diez-Ferrer, & Noelia Cubero de Frutos. (2017). Classification of Confolcal Endomicroscopy Patterns for Diagnosis of Lung Cancer. In 18th World Conference on Lung Cancer.
Abstract: Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.
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Debora Gil, & Antoni Rosell. (2019). Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? In World Lung Cancer Conference.
Abstract: Invited speaker
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Juan J. Villanueva. (2002). Visualization, Imaging and Image Processing..
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