New trends on digitisation of complex engineering drawings

Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that may provide rich source of information for industries. Analysing these drawings often requires applying a set of digital image processing methods to detect and classify symbols and other components. Despite the recent significant advances in image processing, and in particular in deep neural networks, automatic analysis and processing of these engineering drawings is still far from being complete. This paper presents a general framework for complex engineering drawing digitisation. A thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision is presented. Real-life industrial scenario on how to contextualise the digitised information from specific type of these drawings, namely piping and instrumentation diagrams, is discussed in details. A discussion of how new trends on machine vision such as deep learning could be applied to this domain is presented with conclusions and suggestions for future research directions

Spatio-structural Symbol Description with Statistical Feature Add-on

The original publication is available at www.springerlink.comInternational audienceIn this paper, we present a method for symbol description based on both spatio-structural and statistical features computed on elementary visual parts, called 'vocabulary'. This extracted vocabulary is grouped by type (e.g., circle, corner ) and serves as a basis for an attributed relational graph where spatial relational descriptors formalise the links between the vertices, formed by these types, labelled with global shape descriptors. The obtained attributed relational graph description has interesting properties that allows it to be used efficiently for recognising structure and by comparing its attribute signatures. The method is experimentally validated in the context of electrical symbol recognition from wiring diagrams

BoR: Bag-of-Relations for Symbol Retrieval

International audienceIn this paper, we address a new scheme for symbol retrieval based on bag-of-relations (BoRs) which are computed between extracted visual primitives (e.g. circle and corner). Our features consist of pairwise spatial relations from all possible combinations of individual visual primitives. The key characteristic of the overall process is to use topological relation information indexed in bags-of-relations and use this for recognition. As a consequence, directional relation matching takes place only with those candidates having similar topological configurations. A comprehensive study is made by using several different well known datasets such as GREC, FRESH and SESYD, and includes a comparison with state-of-the-art descriptors. Experiments provide interesting results on symbol spotting and other user-friendly symbol retrieval applications

Musings on Symbol Recognition

This paper does not pretend to be yet another survey on symbol recognition methods. It will rather try to take a step back, look at the main efforts done in that area throughout the years and propose some interesting directions to investigate

ChemInk: A Natural Real-Time Recognition System for Chemical Drawings

We describe a new sketch recognition framework for chemical structure drawings that combines multiple levels of visual features using a jointly trained conditional random field. This joint model of appearance at different levels of detail makes our framework less sensitive to noise and drawing variations, improving accuracy and robustness. In addition, we present a novel learning-based approach to corner detection that achieves nearly perfect accuracy in our domain. The result is a recognizer that is better able to handle the wide range of drawing styles found in messy freehand sketches. Our system handles both graphics and text, producing a complete molecular structure as output. It works in real time, providing visual feedback about the recognition progress. On a dataset of chemical drawings our system achieved an accuracy rate of 97.4%, an improvement over the best reported results in literature. A preliminary user study also showed that participants were on average over twice as fast using our sketch-based system compared to ChemDraw, a popular CAD-based tool for authoring chemical diagrams. This was the case even though most of the users had years of experience using ChemDraw and little or no experience using Tablet PCs.National Science Foundation (U.S.) (Grant 0729422)United States. Dept. of Homeland Security (Graduate Research Fellowship)Pfizer Inc

Symbol Recognition: Current Advances and Perspectives

Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content

Combining appearance and context for multi-domain sketch recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 99-102).As our interaction with computing shifts away from the traditional desktop model (e.g., towards smartphones, tablets, touch-enabled displays), the technology that drives this interaction needs to evolve as well. Wouldn't it be great if we could talk, write, and draw to a computer just like we do with each other? This thesis addresses the drawing aspect of that vision: enabling computers to understand the meaning and semantics of free-hand diagrams. We present a novel framework for sketch recognition that seamlessly combines a rich representation of local visual appearance with a probabilistic graphical model for capturing higher level relationships. This joint model makes our system less sensitive to noise and drawing variations, improving accuracy and robustness. The result is a recognizer that is better able to handle the wide range of drawing styles found in messy freehand sketches. To preserve the fluid process of sketching on paper, our interface allows users to draw diagrams just as they would on paper, using the same notations and conventions. For the isolated symbol recognition task our method exceeds state-of-the-art performance in three domains: handwritten digits, PowerPoint shapes, and electrical circuit symbols. For the complete diagram recognition task it was able to achieve excellent performance on both chemistry and circuit diagrams, improving on the best previous results. Furthermore, in an on-line study our new interface was on average over twice as fast as the existing CAD-based method for authoring chemical diagrams, even for novice users who had little or no experience using a tablet. This is one of the first direct comparisons that shows a sketch recognition interface significantly outperforming a professional industry-standard CAD-based tool.by Tom Yu Ouyang.Ph.D

Sketch interpretation using multiscale stochastic models of temporal patterns

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 102-114).Sketching is a natural mode of interaction used in a variety of settings. For example, people sketch during early design and brainstorming sessions to guide the thought process; when we communicate certain ideas, we use sketching as an additional modality to convey ideas that can not be put in words. The emergence of hardware such as PDAs and Tablet PCs has enabled capturing freehand sketches, enabling the routine use of sketching as an additional human-computer interaction modality. But despite the availability of pen based information capture hardware, relatively little effort has been put into developing software capable of understanding and reasoning about sketches. To date, most approaches to sketch recognition have treated sketches as images (i.e., static finished products) and have applied vision algorithms for recognition. However, unlike images, sketches are produced incrementally and interactively, one stroke at a time and their processing should take advantage of this. This thesis explores ways of doing sketch recognition by extracting as much information as possible from temporal patterns that appear during sketching.(cont.) We present a sketch recognition framework based on hierarchical statistical models of temporal patterns. We show that in certain domains, stroke orderings used in the course of drawing individual objects contain temporal patterns that can aid recognition. We build on this work to show how sketch recognition systems can use knowledge of both common stroke orderings and common object orderings. We describe a statistical framework based on Dynamic Bayesian Networks that can learn temporal models of object-level and stroke-level patterns for recognition. Our framework supports multi-object strokes, multi-stroke objects, and allows interspersed drawing of objects - relaxing the assumption that objects are drawn one at a time. Our system also supports real-valued feature representations using a numerically stable recognition algorithm. We present recognition results for hand-drawn electronic circuit diagrams. The results show that modeling temporal patterns at multiple scales provides a significant increase in correct recognition rates, with no added computational penalties.by Tevfik Metin Sezgin.Ph.D