International Joint Conference on Neural Networks

Business process diagrams represent an essential part of business process management activities, including process analysis, process-related communication, and process automation. While the majority of these artifacts are produced with modeling tools, there are still cases where hand-drawn diagrams are created, especially in the initial phases of process discovery and process innovation activities. The transformation of a hand-drawn diagram to digital format often presents several challenges. Thus, our efforts are directed towards investigating the effectiveness of an automatic transformation of hand-drawn diagrams into digital artifacts utilizing optical character recognition. To this aim, we performed empirical research in which subjects were instructed to redraw standardized process elements, which were afterward used for the training and testing set of machine-learning-based character recognition application. The findings obtained in the analysis showed that TensorFlow based and trained solution is capable of identifying hand-drawn process diagrams elements on different levels of accuracy. These insights may be considered when specifying or adapting the visual vocabularies of notations assuring appropriate visual distances between depictions of individual elements.

/pdf/an-empirical-investigation-of-the-effectiveness-of-optical-41nl7x5hh8.pdf

An Empirical Investigation of the Effectiveness of Optical Recognition of Hand-Drawn Business Process Elements by Applying Machine Learning

Hand-drawn Electric Circuit Diagrams Recognition using Deep Learning

We describe an application of the novel Support Vector Machined Kernel (SVM'ed Kernel) to the Recognition of hand-drawn shapes. The SVM'ed kernel function is itself a support vector machine classifier that is learned statistically from data using an automatically generated training set. We show that the new kernel manages to change the classical methodology of defining a feature vector for each pattern. One will only need to define features representing the similarity between two patterns allowing many details to be captured in a concise way. In addition, we illustrate that features describing a single pattern could also be used in this new framework. In this paper we show how the SVM'ed Kernel is defined and trained for the multiclass shape recognition problem. Simulation results show that the SVM'ed Kernel outperforms all other classical kernels and is more robust to hard test sets.

Hand-Drawn Shape Recognition Using the SVM'ed Kernel

With the recent advances in tablet devices industry, sketch recognition has become a potential replacement for existing systems’ traditional user interfaces. Structured diagrams (flow charts, Markov chains, module dependency diagrams, state diagrams, block diagrams, UML, graphs, etc.) are very common in many science fields. Usually, such diagrams are created using structured graphics editors like Microsoft Visio. Structured graphics editors are extremely powerful and expressive, but they can be cumbersome to use. This paper presents an interactive sketch recognition system that converts user’s sketch into structured geometric shapes in usable electronic format with minimal effort.

Interactive Sketch Recognition Framework for Geometric Shapes

Structured diagrams are very prevalent in many document types. Most people who need to create such diagrams use structured graphics editors such as Microsoft Visio. Structured graphics editors are extremely powerful and expressive but they can be cumbersome to use. We have shown through extensive timing experiments that structured diagrams drawn by hand will take only about 10% of the time it takes to draw one using a tool like Visio. This indicates the value of automated recognition of hand-written diagrams. Recently, applications have been developed that use online systems running on pen-input PCs that allow users to create structured diagrams by drawing the diagram on the PC tablet. The progress of offline diagram recognition is still minimal. The objective of this paper is to propose a context-independent off-line diagram recognition system. Our approach utilizes support vector machines for recognition and line primitive extraction by interpretation of line continuation for segmentation.

A new approach for context-independent handwritten offline diagram recognition using support vector machines

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A new approach for context-independent handwritten offline diagram recognition using support vector machines