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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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G.Thorvaldsen and 6 others. 2015. A Tale of two Transcriptions.
Abstract: non-indexed
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources.
Keywords: Nominative Sources; Census; Vital Records; Computer Vision; Optical Character Recognition; Word Spotting
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Gemma Sanchez, Alicia Fornes, Joan Mas and Josep Llados. 2007. Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez, Alicia Fornes, Joan Mas and Josep Llados. 2007. Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez and 6 others. 2003. A system for virtual prototyping of architectural projects. Proceedings of Fifth IAPR International Workshop on Pattern Recognition.65–74.
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Gemma Sanchez, Ernest Valveny, Josep Llados, Joan Mas and N. Lozano. 2004. A platform to extract knowledge from graphic documents. Application to an architectural sketch understanding scenario.
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Gemma Sanchez and Josep Llados. 2001. A Graph Grammar to Recognize Textured Symbols..
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Gemma Sanchez and Josep Llados. 2003. Syntactic models to represent perceptually regular repetitive patterns in graphic documents.
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Gemma Sanchez and Josep Llados. 2004. Syntactic models to represent perceptually regular repetitive patterns in graphic documents.
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Gemma Sanchez, Josep Llados and Enric Marti. 1997. A string-based method to recognize symbols and structural textures in architectural plans. 2nd IAPR Workshop on Graphics Recognition.91–103.
Abstract: This paper deals with the recognition of symbols and struc- tural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clus- tering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the simila- rity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.
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