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C. Alejandro Parraga, Jordi Roca, Dimosthenis Karatzas and Sophie Wuerger. 2014. Limitations of visual gamma corrections in LCD displays. Dis, 35(5), 227–239.
Abstract: A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements.
Keywords: Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados and Alicia Fornes. 2014. A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance. 22nd International Conference on Pattern Recognition.3074–3079.
Abstract: Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.
Keywords: word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance
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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.
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados and Alicia Fornes. 2014. A Novel Learning-free Word Spotting Approach Based on Graph Representation. 11th IAPR International Workshop on Document Analysis and Systems.207–211.
Abstract: Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.
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Marçal Rusiñol, Volkmar Frinken, Dimosthenis Karatzas, Andrew Bagdanov and Josep Llados. 2014. Multimodal page classification in administrative document image streams. IJDAR, 17(4), 331–341.
Abstract: In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages.
Keywords: Digital mail room; Multimodal page classification; Visual and textual document description
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Francisco Cruz and Oriol Ramos Terrades. 2014. EM-Based Layout Analysis Method for Structured Documents. 22nd International Conference on Pattern Recognition.315–320.
Abstract: In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according
to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task.
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Francisco Alvaro, Francisco Cruz, Joan Andreu Sanchez, Oriol Ramos Terrades and Jose Miguel Benedi. 2015. Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars. NEUCOM, 150(A), 147–154.
Abstract: In this paper we de ne a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classi cation features are used to perform an initial classi cation of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models
and the results showed that the proposed grammatical model outperformed
the other methods. Furthermore, grammars also provide the document structure
along with its segmentation.
Keywords: document image analysis; stochastic context-free grammars; text classi cation features
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Lluis Pere de las Heras, Ernest Valveny and Gemma Sanchez. 2014. Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies. Graphics Recognition. Current Trends and Challenges. Springer Berlin Heidelberg, 109–121. (LNCS.)
Abstract: In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions.
Keywords: Graphics recognition; Floor plan analysis; Object segmentation
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Lluis Pere de las Heras, David Fernandez, Alicia Fornes, Ernest Valveny, Gemma Sanchez and Josep Llados. 2014. Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans. Graphics Recognition. Current Trends and Challenges. Springer Berlin Heidelberg, 135–146. (LNCS.)
Abstract: This paper proposes a runlength histogram signature as a perceptual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query, similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition.
Keywords: Graphics recognition; Graphics retrieval; Image classification
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Lluis Gomez and Dimosthenis Karatzas. 2014. Scene Text Recognition: No Country for Old Men? 1st International Workshop on Robust Reading.
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