Acta Scientific Computer Sciences

Research Article Volume 4 Issue 11

Analysis of Segmentation for Text Components in Context of Scene Images Utilizing Connected Components

Mr Jhuntu and Tathagata Roy Chowdhury*

Department of Computer Science and Engineering, St. Mary’s Technical Campus Kolkata, Kolkata, West Bengal, India

*Corresponding Author: Tathagata Roy Chowdhury, Department of Computer Science and Engineering, St. Mary’s Technical Campus Kolkata, Kolkata, West Bengal, India.

Received: September 23, 2022; Published: October 13, 2022

Abstract

Images of nature scenes that contain language contain useful information, such as text-based landmarks, etc. There are several steps involved in text extraction from scene photos. To get effective results, each stage is equally vital. The steps of text detection, localization, segmentation, and recognition are crucial. Due to differences in size, position, and alignment from one image to the next, extracting text from scene photographs is exceedingly challenging. The challenge of extracting text from scene photos is difficult because of all these issues. However, the placement of text in such images is arbitrary and is not restricted to any particular page layout. We used the dataset made available in conjunction with IIIT5K to assess how well the planned "segmentation of scene text images using connected component analysis" performed. Since each image comprises roughly four characters in various font styles and font sizes. A disjunct categorization does not seem to be possible, nevertheless, as even two segmentation procedures that are utterly unrelated can share traits that defy singular categorization 1. As a result, the classification being presented is one that takes into account an approach's emphasis rather than being a perfect split.

Keywords: Image Processing; Connected Components; Image; Binary Image; Segmentation; Grey Scale

References

  1. Hongliang Bai and Liu Changping. "A hybrid license plate extraction method based on edge statistics and morphology”. Pattern Recognition, ICPR. Proceedings of the 17th International Conference on. IEEE 2 (2004).
  2. Bai Jinfeng., et al. "Chinese Image Character Recognition Using DNN and Machine Simulated Training Samples”. Artificial Neural Networks and Machine Learning–ICANN Springer International Publishing, (2014): 209-216.
  3. Gupta Neha and V K Banga. "Image Segmentation for Text Extraction”. Proceedings of the 2nd International Conference on Electrical, Electronics and Civil Engineering (ICEECE'2012), Singapore, April 28-29. (2012).
  4. Dutta A., et al. “Gradient based Approach for Text Detection in Video Frames 1 (2009).
  5. Sivasankaran V., et al. “Recognition of Text in Mobile Captured Images Based on Edge and Connected Component Hybrid Algorithm”. International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE)6 (2014): 358.
  6. Angadi S A and M M Kodabagi. "Text region extraction from low resolution natural scene images using texture features”. Advance Computing Conference (IACC), IEEE 2nd IEEE (2010).
  7. Wei Yi Cheng and Chang Hong Lin. "A robust video text detection approach using SVM”. Expert Systems with Applications12 (2012): 10832-10840.
  8. D Doermann., et al. “Progress in camera based document image analysis”. in Proc. 7th Conf. on Document Analysis and Recognition 1 (2003): 606-687.
  9. S M Lucas., et al. “ICDAR 2003 robust reading competitions”. in Proc. 7th Conf. on Document Analysis and Recognition 2 (2003): 682-687.
  10. Trier and A Jain. “Goal directed evaluation of binarization methods”. IEEE Trans. Pattern Anal. Machine Intell, 17 (1995): 1191-1201.
  11. C Wolf., et al. “Text localization, enhancement and binalization in multimedia document”. in Proc. 16th Conf. on Pattern Recognition 2 (2002): 1037-1040.
  12. S Wu and A Amin. “Automatic thresholding of gray-level using multi-stage approach”. in Proc. 7th Conf. on Document Analysis and Recognition 1 (2003): 493-497.
  13. A Miene., et al. “Extracting textual inserts from digital videos”. in Proc. 6th Conf. on Document Analysis and Recognition (2001): 1079-1083.
  14. A Sato. “A learning method for definite canonicalization based on minimum classification error”. in Proc. 15th Conf. on Pattern Recognition 2 (2000): 199-202.
  15. M Mori. “Video text recognition using feature compensation as category-dependent feature extraction”. in Proc. 7th Conf. on Document Analysis and Recognition 2 (2003): 645-649.
  16. P Simard., et al. “Efficient pattern recognition using a new transformation distance”. Advances in Neural Information Processing Systems, Morgan Kaufmann, 5 (1993): 50-58.
  17. T Wakahara., et al. “Affine-invariant recognition of gray-scale characters using global affine transformation correlation”. IEEE Trans. Pattern (2001).

Citation

Citation: Mr Jhuntu and Tathagata Roy Chowdhury. “Analysis of Segmentation for Text Components in Context of Scene Images Utilizing Connected Components". Acta Scientific Computer Sciences 4.11 (2022): 13-17.

Copyright

Copyright: © 2022 Mr Jhuntu and Tathagata Roy Chowdhury. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

Indexed In




News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is December 25, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue"

Contact US