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.

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Symbol Recognition: Current Advances and Perspectives

Various techniques have been used or simply proposed, during the last 10 years, for recognizing symbols in documents. Differences depend on both application field and researcher's cultural background. We bring the methods most commonly used in the representation, description, and classification phases to the attention of the reader, and we discuss the main recognition strategies. We briefly review a number of papers, that are representative of the various approaches.

Symbol recognition in documents: a collection of techniques?

Symbol recognition is one of the primary stages of any graphics recognition system. This paper reviews the current state of the art in graphic symbol recognition and raises some open issues that need further investigation. Work on symbol recognition tends to be highly application specific. Therefore, this review presents the symbol recognition methods in the context of specific applications.

Graphic Symbol Recognition: An Overview

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.

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New trends on digitisation of complex engineering drawings.

An interactive example-driven approach to graphics recognition in engineering drawings is proposed. The scenario is that the user first interactively provides an example of a graphic object; the system instantly learns its graphical knowledge and uses the acquired knowledge to recognize the same type of graphic objects. The proposed approach represents the graphical knowledge of an object in terms of its structural components and their syntactical relationships. We summarize four types of geometric constraints for knowledge representation, based on which we develop an algorithm for knowledge acquisition. Another algorithm for graphics recognition using the acquired graphical knowledge is also proposed, which is actually a sequential examination of these constraints. In the algorithm, we first guess the next component’s attributes (e.g., size, position and orientation) by reasoning from an earlier found component and the constraint between them, and then search for this hypothetical component in the drawing. If all of the hypothetical components are found, a graphic object of this type is recognized. For improving the system’s recognition accuracy, we develop a user feedback scheme, which can update the graphical knowledge from both positive (missing) and negative (mis-recognized) examples provided by the user for subsequent recognition. Experiments have shown that our proposed approach is both efficient and effective for recognizing various types of graphic objects in engineering drawings.

An interactive example-driven approach to graphics recognition in engineering drawings

A hierarchical neural network approach is presented for the automatic conversion of image documents (ACID), which specifically describes a prototype symbol recognition system (SRS) for automatic computer processing of electrical engineering drawings. This approach achieves a significant reduction of human involvement in the symbol model encoding and recognition processes in contrast to such traditional approaches based on thinning, line tracing, and other structural feature extraction techniques. A set of image intensity moments, which are invariant to geometric transformations, is used as features. A hierarchical neural classifier demonstrates faster and more accurate capabilities for model encoding and recognition. The test results from hand-drawn images by using templates achieves a recognition rate of 98.5% on training symbols and 89% on test symbols. >

T. Cheng

Papers

A symbol recognition system