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Ekta Vats, Anders Hast and Alicia Fornes. 2019. Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion. 15th International Conference on Document Analysis and Recognition.1294–1299.
Abstract: Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.
Keywords: Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching
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Mohammed Al Rawi and Ernest Valveny. 2019. Compact and Efficient Multitask Learning in Vision, Language and Speech. IEEE International Conference on Computer Vision Workshops.2933–2942.
Abstract: Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains.
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Juan Ignacio Toledo. 2019. Information Extraction from Heterogeneous Handwritten Documents. (Ph.D. thesis, Ediciones Graficas Rey.)
Abstract: In this thesis we explore information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes roughly equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words.
In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction.
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Albert Berenguel. 2019. Analysis of background textures in banknotes and identity documents for counterfeit detection. (Ph.D. thesis, Ediciones Graficas Rey.)
Abstract: Counterfeiting and piracy are a form of theft that has been steadily growing in recent years. A counterfeit is an unauthorized reproduction of an authentic/genuine object. Banknotes and identity documents are two common objects of counterfeiting. The former is used by organized criminal groups to finance a variety of illegal activities or even to destabilize entire countries due the inflation effect. Generally, in order to run their illicit businesses, counterfeiters establish companies and bank accounts using fraudulent identity documents. The illegal activities generated by counterfeit banknotes and identity documents has a damaging effect on business, the economy and the general population. To fight against counterfeiters, governments and authorities around the globe cooperate and develop security features to protect their security documents. Many of the security features in identity documents can also be found in banknotes. In this dissertation we focus our efforts in detecting the counterfeit banknotes and identity documents by analyzing the security features at the background printing. Background areas on secure documents contain fine-line patterns and designs that are difficult to reproduce without the manufacturers cutting-edge printing equipment. Our objective is to find the loose of resolution between the genuine security document and the printed counterfeit version with a publicly available commercial printer. We first present the most complete survey to date in identity and banknote security features. The compared algorithms and systems are based on computer vision and machine learning. Then we advance to present the banknote and identity counterfeit dataset we have built and use along all this thesis. Afterwards, we evaluate and adapt algorithms in the literature for the security background texture analysis. We study this problem from the point of view of robustness, computational efficiency and applicability into a real and non-controlled industrial scenario, proposing key insights to use these algorithms. Next, within the industrial environment of this thesis, we build a complete service oriented architecture to detect counterfeit documents. The mobile application and the server framework intends to be used even by non-expert document examiners to spot counterfeits. Later, we re-frame the problem of background texture counterfeit detection as a full-reference game of spotting the differences, by alternating glimpses between a counterfeit and a genuine background using recurrent neural networks. Finally, we deal with the lack of counterfeit samples, studying different approaches based on anomaly detection.
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Sangeeth Reddy, Minesh Mathew, Lluis Gomez, Marçal Rusiñol, Dimosthenis Karatzas and C.V. Jawahar. 2020. RoadText-1K: Text Detection and Recognition Dataset for Driving Videos. IEEE International Conference on Robotics and Automation.
Abstract: Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/
projects/cvit-projects/roadtext-1k
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Raul Gomez, Jaume Gibert, Lluis Gomez and Dimosthenis Karatzas. 2020. Location Sensitive Image Retrieval and Tagging. 16th European Conference on Computer Vision.
Abstract: People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging.
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Lei Kang, Pau Riba, Marçal Rusiñol, Alicia Fornes and Mauricio Villegas. 2020. Distilling Content from Style for Handwritten Word Recognition. 17th International Conference on Frontiers in Handwriting Recognition.
Abstract: Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten
word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes.
Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both
the generative process and the recognition efficiency in the IAM dataset.
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Lei Kang, Pau Riba, Yaxing Wang, Marçal Rusiñol, Alicia Fornes and Mauricio Villegas. 2020. GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images. 16th European Conference on Computer Vision.
Abstract: 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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Josep Llados, Daniel Lopresti and Seiichi Uchida, eds. 2021. 16th International Conference, 2021, Proceedings, Part III. Springer Cham. (LNCS.)
Abstract: This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.
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Josep Llados, Daniel Lopresti and Seiichi Uchida, eds. 2021. 16th International Conference, 2021, Proceedings, Part IV. Springer Cham. (LNCS.)
Abstract: This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.
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