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Fernando Vilariño, Dimosthenis Karatzas and Alberto Valcarce. 2018. Libraries as New Innovation Hubs: The Library Living Lab. 30th ISPIM Innovation Conference.
Abstract: Libraries are in deep transformation both in EU and around the world, and they are thriving within a great window of opportunity for innovation. In this paper, we show how the Library Living Lab in Barcelona participated of this changing scenario and contributed to create the Bibliolab program, where more than 200 public libraries give voice to their users in a global user-centric innovation initiative, using technology as enabling factor. The Library Living Lab is a real 4-helix implementation where Universities, Research Centers, Public Administration, Companies and the Neighbors are joint together to explore how technology transforms the cultural experience of people. This case is an example of scalability and provides reference tools for policy making, sustainability, user engage methodologies and governance. We provide specific examples of new prototypes and services that help to understand how to redefine the role of the Library as a real hub for social innovation.
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Alicia Fornes and Bart Lamiroy. 2018. Graphics Recognition, Current Trends and Evolutions. Springer International Publishing. (LNCS.)
Abstract: This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017.
The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps.
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Y. Patel, Lluis Gomez, Raul Gomez, Marçal Rusiñol, Dimosthenis Karatzas and C.V. Jawahar. 2018. TextTopicNet-Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces.
Abstract: The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community.
In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN.
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Suman Ghosh. 2018. Word Spotting and Recognition in Images from Heterogeneous Sources A. (Ph.D. thesis, Ediciones Graficas Rey.)
Abstract: Text is the most common way of information sharing from ages. With recent development of personal images databases and handwritten historic manuscripts the demand for algorithms to make these databases accessible for browsing and indexing are in rise. Enabling search or understanding large collection of manuscripts or image databases needs fast and robust methods. Researchers have found different ways to represent cropped words for understanding and matching, which works well when words are already segmented. However there is no trivial way to extend these for non-segmented documents. In this thesis we explore different methods for text retrieval and recognition from unsegmented document and scene images. Two different ways of representation exist in literature, one uses a fixed length representation learned from cropped words and another a sequence of features of variable length. Throughout this thesis, we have studied both these representation for their suitability in segmentation free understanding of text. In the first part we are focused on segmentation free word spotting using a fixed length representation. We extended the use of the successful PHOC (Pyramidal Histogram of Character) representation to segmentation free retrieval. In the second part of the thesis, we explore sequence based features and finally, we propose a unified solution where the same framework can generate both kind of representations.
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Francisco Cruz and Oriol Ramos Terrades. 2018. A probabilistic framework for handwritten text line segmentation.
Abstract: We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step.
Keywords: Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning
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Lei Kang, Marçal Rusiñol, Alicia Fornes, Pau Riba and Mauricio Villegas. 2020. Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition. IEEE Winter Conference on Applications of Computer Vision.
Abstract: Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step.
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Raul Gomez, Jaume Gibert, Lluis Gomez and Dimosthenis Karatzas. 2020. Exploring Hate Speech Detection in Multimodal Publications. IEEE Winter Conference on Applications of Computer Vision.
Abstract: In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research.
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Marçal Rusiñol, Lluis Gomez, A. Landman, M. Silva Constenla and Dimosthenis Karatzas. 2019. Automatic Structured Text Reading for License Plates and Utility Meters. BMVC Workshop on Visual Artificial Intelligence and Entrepreneurship.
Abstract: Reading text in images has attracted interest from computer vision researchers for
many years. Our technology focuses on the extraction of structured text – such as serial
numbers, machine readings, product codes, etc. – so that it is able to center its attention just on the relevant textual elements. It is conceived to work in an end-to-end fashion, bypassing any explicit text segmentation stage. In this paper we present two different industrial use cases where we have applied our automatic structured text reading technology. In the first one, we demonstrate an outstanding performance when reading license plates compared to the current state of the art. In the second one, we present results on our solution for reading utility meters. The technology is commercialized by a recently created spin-off company, and both solutions are at different stages of integration with final clients.
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Ali Furkan Biten and 8 others. 2019. ICDAR 2019 Competition on Scene Text Visual Question Answering. 3rd Workshop on Closing the Loop Between Vision and Language, in conjunction with ICCV2019.
Abstract: This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed
by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23, 038 images annotated with 31, 791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios.
The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that
can exploit scene text to achieve holistic image understanding.
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Andres Mafla, Sounak Dey, Ali Furkan Biten, Lluis Gomez and Dimosthenis Karatzas. 2020. Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features. IEEE Winter Conference on Applications of Computer Vision.
Abstract: Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval.
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