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Manuel Carbonell, Mauricio Villegas, Alicia Fornes and Josep Llados. 2018. Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model. 13th IAPR International Workshop on Document Analysis Systems.399–404.
Abstract: When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different
configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing.
Keywords: Named entity recognition; Handwritten Text Recognition; neural networks
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Raul Gomez, Lluis Gomez, Jaume Gibert and Dimosthenis Karatzas. 2018. Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods. 15th European Conference on Computer Vision Workshops.530–544. (LNCS.)
Abstract: Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis.
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Anguelos Nicolaou, Sounak Dey, V.Christlein, A.Maier and Dimosthenis Karatzas. 2018. Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings. International Workshop on Reproducible Research in Pattern Recognition.71–82. (LNCS.)
Abstract: Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits.
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Dena Bazazian, Dimosthenis Karatzas and Andrew Bagdanov. 2018. Word Spotting in Scene Images based on Character Recognition. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.1872–1874.
Abstract: In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images.
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Marçal Rusiñol, Dimosthenis Karatzas and Josep Llados. 2015. Automatic Verification of Properly Signed Multi-page Document Images. Proceedings of the Eleventh International Symposium on Visual Computing.327–336. (LNCS 9475.)
Abstract: In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages.
Keywords: Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow
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Giacomo Magnifico, Beata Megyesi, Mohamed Ali Souibgui, Jialuo Chen and Alicia Fornes. 2022. Lost in Transcription of Graphic Signs in Ciphers. International Conference on Historical Cryptology (HistoCrypt 2022).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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Ilke Demir, Dena Bazazian, Adriana Romero, Viktoriia Sharmanska and Lyne P. Tchapmi. 2018. WiCV 2018: The Fourth Women In Computer Vision Workshop. 4th Women in Computer Vision Workshop.1941–19412.
Abstract: We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
Keywords: Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning
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Arnau Baro, Pau Riba and Alicia Fornes. 2018. A Starting Point for Handwritten Music Recognition. 1st International Workshop on Reading Music Systems.5–6.
Abstract: In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community.
Keywords: Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA
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Raul Gomez, Ali Furkan Biten, Lluis Gomez, Jaume Gibert, Marçal Rusiñol and Dimosthenis Karatzas. 2019. Selective Style Transfer for Text. 15th International Conference on Document Analysis and Recognition.805–812.
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.
Keywords: transfer; text style transfer; data augmentation; scene text detection
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Arnau Baro, Jialuo Chen, Alicia Fornes and Beata Megyesi. 2019. Towards a generic unsupervised method for transcription of encoded manuscripts. 3rd International Conference on Digital Access to Textual Cultural Heritage.73–78.
Abstract: Historical ciphers, a special type of manuscripts, contain encrypted information, important for the interpretation of our history. The first step towards decipherment is to transcribe the images, either manually or by automatic image processing techniques. Despite the improvements in handwritten text recognition (HTR) thanks to deep learning methodologies, the need of labelled data to train is an important limitation. Given that ciphers often use symbol sets across various alphabets and unique symbols without any transcription scheme available, these supervised HTR techniques are not suitable to transcribe ciphers. In this paper we propose an un-supervised method for transcribing encrypted manuscripts based on clustering and label propagation, which has been successfully applied to community detection in networks. We analyze the performance on ciphers with various symbol sets, and discuss the advantages and drawbacks compared to supervised HTR methods.
Keywords: A. Baró, J. Chen, A. Fornés, B. Megyesi.
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