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Dissected Scene Character Recognition Using HOG Descriptors

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Internet of Things and Its Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 825))

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Abstract

Automatic scene text recognition is an interesting problem in computer vision and Internet of things. It may facilitate intelligent interaction between machines and mankind in today’s cloud-enabled civilization. In this paper, we present a method for dissected scene character recognition. At first, color images are converted into grayscale and then some noise removal and pre-processing operations are applied. Next, we normalize them to bring them to a uniform dimension and compute features for training and prediction. Experimenting on scene characters at three different levels of complexities i.e. relatively good images, relatively bad images, and combined images with multiple classifiers such as naïve Bayes, KNN, MLP, random forest and SVM, detail results are reported. Highest accuracies i.e. 74.48% for good images only, 59.13% on bad images only and 71.52% for overall images, are obtained with the SVM classifier. Comparison with similar state-of-the-art methods is also included and our method is found to outperform others.

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Acknowledgements

The authors are thankful to the Department of Computer Science and Engineering of Aliah University, Kolkata, India, for providing every kind of support for carrying out this research work. P. Sengupta is further grateful to Dept. of MA & ME, Govt. of West Bengal for providing Swami Vivekananda Merit cum Means Fellowship.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Aliah University, IIA/27 New Town, Kolkata, 700160, India

    Payel Sengupta & Ayatullah Faruk Mollah

Authors
  1. Payel Sengupta
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  2. Ayatullah Faruk Mollah
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Editor information

Editors and Affiliations

  1. School of Computing, Engineering, Physical Sciences, Artificial Intelligence, Visual Communication and Networks (AVCN) Research Centre, University of the West of Scotland, Paisley, Renfrewshire, UK

    Keshav Dahal

  2. Department of Information Technology, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Debasis Giri

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sarmistha Neogy

  4. Department of Computer Science and Engineering, National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand, India

    Subrata Dutta

  5. Department of Computer Science and Engineering, National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand, India

    Sanjay Kumar

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Sengupta, P., Mollah, A.F. (2022). Dissected Scene Character Recognition Using HOG Descriptors. In: Dahal, K., Giri, D., Neogy, S., Dutta, S., Kumar, S. (eds) Internet of Things and Its Applications. Lecture Notes in Electrical Engineering, vol 825. Springer, Singapore. https://doi.org/10.1007/978-981-16-7637-6_18

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  • DOI: https://doi.org/10.1007/978-981-16-7637-6_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7636-9

  • Online ISBN: 978-981-16-7637-6

  • eBook Packages: EngineeringEngineering (R0)

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