Automatic Structural Scene Digitalization

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.Comment: paper submitted to PloS On

Shape Retrieval Methods for Architectural 3D Models

This thesis introduces new methods for content-based retrieval of architecture-related 3D models. We thereby consider two different overall types of architectural 3D models. The first type consists of context objects that are used for detailed design and decoration of 3D building model drafts. This includes e.g. furnishing for interior design or barriers and fences for forming the exterior environment. The second type consists of actual building models. To enable efficient content-based retrieval for both model types that is tailored to the user requirements of the architectural domain, type-specific algorithms must be developed. On the one hand, context objects like furnishing that provide similar functions (e.g. seating furniture) often share a similar shape. Nevertheless they might be considered to belong to different object classes from an architectural point of view (e.g. armchair, elbow chair, swivel chair). The differentiation is due to small geometric details and is sometimes only obvious to an expert from the domain. Building models on the other hand are often distinguished according to the underlying floor- and room plans. Topological floor plan properties for example serve as a starting point for telling apart residential and commercial buildings. The first contribution of this thesis is a new meta descriptor for 3D retrieval that combines different types of local shape descriptors using a supervised learning approach. The approach enables the differentiation of object classes according to small geometric details and at the same time integrates expert knowledge from the field of architecture. We evaluate our approach using a database containing arbitrary 3D models as well as on one that only consists of models from the architectural domain. We then further extend our approach by adding a sophisticated shape descriptor localization strategy. Additionally, we exploit knowledge about the spatial relationship of object components to further enhance the retrieval performance. In the second part of the thesis we introduce attributed room connectivity graphs (RCGs) as a means to characterize a 3D building model according to the structure of its underlying floor plans. We first describe how RCGs are inferred from a given building model and discuss how substructures of this graph can be queried efficiently. We then introduce a new descriptor denoted as Bag-of-Attributed-Subgraphs that transforms attributed graphs into a vector-based representation using subgraph embeddings. We finally evaluate the retrieval performance of this new method on a database consisting of building models with different floor plan types. All methods presented in this thesis are aimed at an as automated as possible workflow for indexing and retrieval such that only minimum human interaction is required. Accordingly, only polygon soups are required as inputs which do not need to be manually repaired or structured. Human effort is only needed for offline groundtruth generation to enable supervised learning and for providing information about the orientation of building models and the unit of measurement used for modeling

Contextual word spotting in historical handwritten documents

Existen incontables colecciones de documentos históricos en archivos y librerías repletos de valiosa información para historiadores e investigadores. La extracción de esta información se ha convertido en una de las principales tareas para investigadores del área de análisis de documentos. Hay un interés creciente en digitalizar, conservar y dar acceso a este tipo de documentos. Pero sólo la digitalización no es suficiente para los investigadores. La extracción y/o indexación de la información de estos documentos tiene un creciente interés entre los investigadores. En muchos casos, y en particular en documentos históricos, la completa trascripción de estos documentos es extremadamente difícil debido a dificultades intrínsecas: preservación física pobre, diferentes estilos de escritura, lenguajes obsoletos, etc. La búsqueda de palabras se convierte en una popular y eficiente alternativa a la tran-scripción completa. Este método conlleva una inherente degradación de las imágenes. La búsqueda de palabras se formula holísticamente como una búsqueda visual de una forma dada en un conjunto grande de imágenes, en vez de reconocer el texto y buscar la palabra mediante la comparación de códigos ascii. Pero el rendimiento de los métodos de búsqueda de palabras clásicos puede verse afectado por el nivel de degradación de las imágenes, que en algunos casos pueden ser inaceptables. Por esta razón, proponemos una búsqueda de palabras contextual que utiliza la información contextual/semántica para obtener resultados donde los métodos de búsqueda clásica no lo logran un rendimiento aceptable. El sistema de búsqueda de palabras contextual propuesto en esta tesis utiliza un método de búsqueda de palabras basado en segmentación, y por tanto es necesaria una segmentación de palabras precisa. Documentos históricos manuscritos presentan algunas dificultades que pueden dificultar la extracción de palabras. Proponemos un método de segmentación de palabras que formula el problema como la búsqueda del camino central en el area que hay entre dos líneas consecutivas. Esto se resuelve mediante un problema de grafo transversal. Un algoritmo de búsqueda de caminos es utilizado para encontrar el camino óptimo en el grafo, calculado previamente, entre dos líneas de texto. Una vez las líneas se han extraído, las palabras son localizadas dentro de las líneas de texto utilizando un método del estado del arte para segmentar palabras. Los métodos de búsqueda clásicos pueden mejor utilizando la información contextual de los documentos. Presentamos un nuevo sistema, orientado a documentos manuscritos que presentan una estructura a los largo de sus páginas, para extraer la información uti-lizando información contextual. El sistema es una eficiente herramienta para la transcripción semiautomática que utiliza la información contextual para obtener mejores resultados que los métodos de búsqueda convencionales. La información contextual es descubierta automáticamente reconociendo estructuras repetitivas y categorizando las palabras con su correspondiente clase semántica. Se extraen las palabras más frecuentes de cada clase semántica y así el mismo texto es utilizado para transcribir todas ellas. Los resultados experimentales obtenidos en esta tesis mejoran los resultados de los métodos clásicos de búsqueda de palabras, demostrando idoneidad de la arquitectura propuesta para la búsqueda de palabras en documentos históricos manuscritos utilizando la información contextual.There are countless collections of historical documents in archives and libraries that contain plenty of valuable information for historians and researchers. The extraction of this information has become a central task among the Document Analysis researches and practitioners. There is an increasing interest to digital preserve and provide access to these kind of documents. But only the digitalization is not enough for the researchers. The extraction and/or indexation of information of this documents has had an increased interest among researchers. In many cases, and in particular in historical manuscripts, the full transcription of these documents is extremely di cult due the inherent de ciencies: poor physical preservation, di erent writing styles, obsolete languages, etc. Word spotting has become a popular an e cient alternative to full transcription. It inherently involves a high level of degradation in the images. The search of words is holistically formulated as a visual search of a given query shape in a larger image, instead of recognising the input text and searching the query word with an ascii string comparison. But the performance of classical word spotting approaches depend on the degradation level of the images being unacceptable in many cases . In this thesis we have proposed a novel paradigm called contextual word spotting method that uses the contextual/semantic information to achieve acceptable results whereas classical word spotting does not reach. The contextual word spotting framework proposed in this thesis is a segmentation-based word spotting approach, so an e cient word segmentation is needed. Historical handwritten documents present some common di culties that can increase the di culties the extraction of the words. We have proposed a line segmentation approach that formulates the problem as nding the central part path in the area between two consecutive lines. This is solved as a graph traversal problem. A path nding algorithm is used to nd the optimal path in a graph, previously computed, between the text lines. Once the text lines are extracted, words are localized inside the text lines using a word segmentation technique from the state of the art. Classical word spotting approaches can be improved using the contextual information of the documents. We have introduced a new framework, oriented to handwritten documents that present a highly structure, to extract information making use of context. The framework is an e cient tool for semi-automatic transcription that uses the contextual information to achieve better results than classical word spotting approaches. The contextual information is automatically discovered by recognizing repetitive structures and categorizing all the words according to semantic classes. The most frequent words in each semantic cluster are extracted and the same text is used to transcribe all them. The experimental results achieved in this thesis outperform classical word spotting approaches demonstrating the suitability of the proposed ensemble architecture for spotting words in historical handwritten documents using contextual information