Multi-domain Hierarchical Free-Sketch Recognition Using Graphical Models

Alvarado, Christine

doi:10.1007/978-1-84882-812-4_2

Cited by 2 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [12] HMM model is used to segment the components, it allows symbol hypothesis generation using 2D dynamic programming and components can be classified using neural network back propagation algorithm. [13], [14] Have proposed a method in which the objects are recognized based on its physical appearance by considering length breadth and connecting leads of components.…”

Section: Related Workmentioning

confidence: 99%

Handwritten Electric Circuit Diagram Recognition: An Approach Based on Finite State Machine

R¹,

Dinesh²,

Prabhanjan³

2019

IJMLC

View full text Add to dashboard Cite

In this paper we propose a method for recognizing hand drawn electronic circuit diagrams. The proposed method first detect and classify each components present in the hand drawn circuit diagram. For the purpose of component recognition, we have constructed the feature vector by combining Local Binary Pattern (LBP) and statistical features based on pixel density. Classification of components is done by using support vector machine (SVM) classifier. Upon detection and recognition of components, the proposed method subsequently uses the position and sequence of arrangement of components to determine the type of circuit. For the purpose of establishing the sequence of components we have used finite state machine. The proposed method represents the sequence of recognized components as a string. This string representation of circuit is fed to a Finite State Machine (FSM) to detect type of circuit. The proposed method has been tested on about 100 hand written circuit diagrams of varying complexities and of different types. The proposed component detection method gives over 99% accuracy whereas, the circuit recognition method has recognition rate of over 85% recognition rate for the circuit type recognition.Index Terms-Circuit recognition, finite state machine, Local binary pattern, SVM, statistical features.

show abstract

Section: Related Workmentioning

confidence: 99%

Handwritten Electric Circuit Diagram Recognition: An Approach Based on Finite State Machine

R¹,

Dinesh²,

Prabhanjan³

2019

IJMLC

View full text Add to dashboard Cite

show abstract

“…Sketching on paper or a pen‐input display such as table PCs and smart phones is a natural way to quickly externalise ideas, and it is often used in the early prototyping stages to express and record design ideas and can be adopted by many domains including mechanical engineering [1], software design [2, 3], information architecture [4, 5] and pen‐based recognition and simulation systems [6]. As a recent trend against traditional window, icon, menu and pointer paradigm, sketching plays a growing important role in two‐dimensional (2D)/three‐dimensional (3D) modelling and human–computer interface [7].…”

Section: Introductionmentioning

confidence: 99%

Online stroke segmentation by quick penalty‐based dynamic programming

Wenyin

Lü

et al. 2013

IET Computer Vision

View full text Add to dashboard Cite

A stroke segmentation method named quick penalty‐based dynamic programming is proposed for splitting a sketchy stroke into several regular primitive shapes, such as line segments and elliptical arcs. The authors extend the dynamic programming framework with a customisable penalty function, which measures the correctness of splitting a stroke at a particular point. With the help of the penalty function, the proposed dynamic programming framework can finish the stroke segmentation process without any prior knowledge of the number and/or the type of segments contained in the sketchy stroke. Its response time is sufficiently short for online applications, even for long strokes. Experiments show that the proposed method is robust for strokes with arbitrary shape and size.

show abstract

Multi-domain Hierarchical Free-Sketch Recognition Using Graphical Models

Cited by 2 publications

References 30 publications

Handwritten Electric Circuit Diagram Recognition: An Approach Based on Finite State Machine

Handwritten Electric Circuit Diagram Recognition: An Approach Based on Finite State Machine

Online stroke segmentation by quick penalty‐based dynamic programming

Contact Info

Product

Resources

About