|
|
Alicia Fornes, Josep Llados, Joan Mas, Joana Maria Pujadas-Mora and Anna Cabre. 2014. A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts. Digital Access to Textual Cultural Heritage Conference.103–108.
Abstract: In this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts.
|
|
|
|
Ariel Amato, Angel Sappa, Alicia Fornes, Felipe Lumbreras and Josep Llados. 2013. Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform. 2nd International ACM Workshop on Crowdsourcing for Multimedia.21–22.
Abstract: In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized.
|
|
|
|
David Fernandez, Simone Marinai, Josep Llados and Alicia Fornes. 2013. Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks. 2nd International Workshop on Historical Document Imaging and Processing.36–43.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.
|
|
|
|
Volkmar Frinken, Andreas Fischer and Carlos David Martinez Hinarejos. 2013. Handwriting Recognition in Historical Documents using Very Large Vocabularies. 2nd International Workshop on Historical Document Imaging and Processing.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.
|
|
|
|
Thanh Ha Do, Salvatore Tabbone and Oriol Ramos Terrades. 2013. Document noise removal using sparse representations over learned dictionary. Symposium on Document engineering.161–168.
Abstract: best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental
results on several datasets demonstrate the robustness of our method compared with the state-of-the-art.
|
|
|
|
Alicia Fornes, Volkmar Frinken, Andreas Fischer, Jon Almazan, G. Jackson and Horst Bunke. 2011. A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors. Proceedings of the 2011 Workshop on Historical Document Imaging and Processing. ACM, 83–90.
Abstract: The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach.
|
|
|
|
Andreas Fischer, Volkmar Frinken, Alicia Fornes and Horst Bunke. 2011. Transcription Alignment of Latin Manuscripts Using Hidden Markov Models. Proceedings of the 2011 Workshop on Historical Document Imaging and Processing. ACM, 29–36.
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.
|
|
|
|
Oriol Ramos Terrades, Alejandro Hector Toselli, Nicolas Serrano, Veronica Romero, Enrique Vidal and Alfons Juan. 2010. Interactive layout analysis and transcription systems for historic handwritten documents. 10th ACM Symposium on Document Engineering.219–222.
Abstract: The amount of digitized legacy documents has been rising dramatically over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents, waiting to be classified and finally transcribed into a textual electronic format (such as ASCII or PDF). Nevertheless, most of the available fully-automatic applications addressing this task are far from being perfect and heavy and inefficient human intervention is often required to check and correct the results of such systems. In contrast, multimodal interactive-predictive approaches may allow the users to participate in the process helping the system to improve the overall performance. With this in mind, two sets of recent advances are introduced in this work: a novel interactive method for text block detection and two multimodal interactive handwritten text transcription systems which use active learning and interactive-predictive technologies in the recognition process.
Keywords: Handwriting recognition; Interactive predictive processing; Partial supervision; Interactive layout analysis
|
|
|
|
Albert Gordo, Jaume Gibert, Ernest Valveny and Marçal Rusiñol. 2010. A Kernel-based Approach to Document Retrieval. 9th IAPR International Workshop on Document Analysis Systems.377–384.
Abstract: In this paper we tackle the problem of document image retrieval by combining a similarity measure between documents and the probability that a given document belongs to a certain class. The membership probability to a specific class is computed using Support Vector Machines in conjunction with similarity measure based kernel applied to structural document representations. In the presented experiments, we use different document representations, both visual and structural, and we apply them to a database of historical documents. We show how our method based on similarity kernels outperforms the usual distance-based retrieval.
|
|
|
|
Farshad Nourbakhsh, Dimosthenis Karatzas and Ernest Valveny. 2010. A polar-based logo representation based on topological and colour features. 9th IAPR International Workshop on Document Analysis Systems.341–348.
Abstract: In this paper, we propose a novel rotation and scale invariant method for colour logo retrieval and classification, which involves performing a simple colour segmentation and subsequently describing each of the resultant colour components based on a set of topological and colour features. A polar representation is used to represent the logo and the subsequent logo matching is based on Cyclic Dynamic Time Warping (CDTW). We also show how combining information about the global distribution of the logo components and their local neighbourhood using the Delaunay triangulation allows to improve the results. All experiments are performed on a dataset of 2500 instances of 100 colour logo images in different rotations and scales.
|
|
