SKETRACK: Stroke-Based Recognition of Online Hand-Drawn Sketches of Arrow-Connected Diagrams and Digital Logic Circuit Diagrams

Author:

Altun Oğuz1,Nooruldeen Orhan1ORCID

Affiliation:

1. Department of Computer Engineering, Faculty of Electrical and Electronic Engineering, Yildiz Technical University, Istanbul, Turkey

Abstract

Digitalization of handwritten documents has created a greater need for accurate online recognition of hand-drawn sketches. However, the online recognition of hand-drawn diagrams is an enduring challenge in human-computer interaction due to the complexity in extracting and recognizing the visual objects reliably from a continuous stroke stream. This paper focuses on the design and development of a new, efficient stroke-based online hand-drawn sketch recognition scheme named SKETRACK for hand-drawn arrow diagrams and digital logic circuit diagrams. The fundamental parts of this model are text separation, symbol segmentation, feature extraction, classification, and structural analysis. The proposed scheme utilizes the concepts of normalization and segmentation to isolate the text from the sketches. Then, the features are extracted to model different structural variations of the strokes that are categorized into the arrows/lines and the symbols for effective processing. The strokes are clustered using the spectral clustering algorithm based on p-distance and Euclidean distance to compute the similarity between the features and minimize the feature dimensionality by grouping similar features. Then, the symbol recognition is performed using modified support vector machine (MSVM) classifier in which a hybrid kernel function with a lion optimized tuning parameter of SVM is utilized. Structural analysis is performed with lion-based task optimization for recognizing the symbol candidates to form the final diagram representations. This proposed recognition model is suitable for simpler structures such as flowcharts, finite automata, and the logic circuit diagrams. Through the experiments, the performance of the proposed SKETRACK scheme is evaluated on three domains of databases and the results are compared with the state-of-the-art methods to validate its superior efficiency.

Publisher

Hindawi Limited

Subject

Computer Science Applications,Software

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Exploration of advancements in handwritten document recognition techniques;Intelligent Systems with Applications;2024-06

2. Computer multimedia aided design and hand-drawn effect analysis based on grid resource sharing cooperative algorithm;Journal of Intelligent & Fuzzy Systems;2024-03-05

3. An Overview of Hand-Drawn Diagram Recognition Methods and Applications;IEEE Access;2024

4. Img2Sim-V2: A CAD Tool for User-Independent Simulation of Circuits in Image Format;2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD);2023-07-03

5. From Image to Simulation: An ANN-based Automatic Circuit Netlist Generator (Img2Sim);2022 18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD);2022-06-12

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3