Image Segmentation and Text Extraction Techniques for Efficient Information Retrieval
DOI:
https://doi.org/10.61453/jods.v2025no15Keywords:
Image Segmentation, Text Extraction, OCR, Visual Accessibility, Scene Images, Information RetrievalAbstract
Image segmentation and text extraction are important tasks in the field of computer vision and image processing. Image segmentation and text extraction involve identifying and separating objects or regions within an image, and helping visually impaired individuals access visual content. The ability to accurately extract text from scene images can have various applications and benefits. This can help automatically generate captions or descriptions for images, making them more accessible to individuals with visual impairments. This can help in tasks such as indexing and image search, where the extracted text can be used to improve the accuracy and relevance of search results. In addition, the extracted text can be used for sentiment analysis or other forms of text-based analysis, providing valuable insights into the content and context of the images. As the techniques discussed in the paper have the potential to help enhance the utility and usability of scene images in various applications and domains.
References
AlMeraj, Z., Wyvill, B., Isenberg, T., Gooch, A. A., & Guy, R. (2009). Automatically mimicking unique hand-drawn pencil lines. Computers and Graphics (Pergamon), 33(4), 496–508. https://doi.org/10.1016/j.cag.2009.04.004
Behera, A., Lalanne, D., & Ingold, R. (2005). Combining Color and Layout Features for the Identification of Low-resolution Documents. International Journal of Signal Processing, 2(1), ISSN: 1304-4478: 7-14.
Coates, D. R., Chin, J. M., & Chung, S. T. L. (2011). Discovery of an in vivo Chemical Probe of the Lysine Methyltransferases G9a and GLP. Bone, 23(1), 1–7.
Hamad, K., & Kaya, M. (2016). A Detailed Analysis of Optical Character Recognition Technology. International Journal of Applied Mathematics, Electronics and Computers, 4(Special Issue-1), 244–244. https://doi.org/10.18100/ijamec.270374
Hoda, R., Salleh, N., Grundy, J., & Tee, H. M. (2017). Systematic literature reviews in agile software development: A tertiary study. Information and Software Technology, 85, 60–70. https://doi.org/10.1016/j.infsof.2017.01.007
Juang, J., Tsai, Y., & Fan, Y. (2015). Visual Recognition and Its Application to Robot Arm Control. 851–880. https://doi.org/10.3390/app5040851
Jung, K., Kim, K. I., & Jain, A. K. (2004). Text information extraction in images and video: A survey. Pattern Recognition, 37(5), 977–997. https://doi.org/10.1016/j.patcog.2003.10.012
Justo, P. (2015). Raspberry Pi vs Arduino. December 4, 2015, 1. Retrieved from https://makezine.com/2015/12/04/admittedly-simplistic-guide-raspberry-pi-vs-arduino/
K. Bhargavi, & S. Jyothi. (2014). A Survey on Threshold Based Segmentation Technique in Image Processing. International Journal of Innovative Research & Development, 3(12), 234–239. https://www.researchgate.net/profile/Singaraju-Jyothi/publication/309209325
Kim, K. B. (2015). Image binarization using intensity range of grayscale images. International Journal of Multimedia and Ubiquitous Engineering, 10(7), 139–144. https://doi.org/10.14257/ijmue.2015.10.7.15
Kulkarni, S. S., Harale, A. D., & Thakur, A. V. (2018). Image processing for driver’s safety and vehicle control using raspberry Pi and webcam. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, ICPCSI 2017, 1, 1288–1291. https://doi.org/10.1109/ICPCSI.2017.8391917
Libouga, D., Marius Otesteanu, Ideal Oscar Libouga, Laurent Bitjoka, & Popa, G. D. (2018). A Review on Image Segmentation Techniques and Performance Measures. Zenodo (CERN European Organization for Nuclear Research), 12(12), 1107–1117. https://doi.org/10.5281/zenodo.2571650
Liu, G., Jiang, M., Cun, H., Shi, Z., & Hao, J. (2017). An Automatic Text Region Positioning Method for the Low-Contrast Image. Journal of Computer and Communications, 05(10), 36–49. https://doi.org/10.4236/jcc.2017.510005
Lu, S., Chen, T., Tian, S., Lim, J. H., & Tan, C. L. (2015). Scene text extraction based on edges and support vector regression. International Journal on Document Analysis and Recognition, 18(2), 125–135. https://doi.org/10.1007/s10032-015-0237-z
Lue, H.-T. (2010). A Novel Character Segmentation Method for Text Images Captured by Cameras. ETRI Journal, 32(5), 729–739. https://doi.org/10.4218/etrij.10.1510.0086
Mancas-Thillou, C., & Gosselin, B. (2007). Color text extraction with selective metric-based clustering. Computer Vision and Image Understanding, 107(1–2), 97–107. https://doi.org/10.1016/j.cviu.2006.11.010
Mlyahilu, J. N., Mlyahilu, J. N., Lee, J. E., Kim, Y. B., & Kim, J. N. (2022). Morphological geodesic active contour algorithm for the segmentation of the histogram-equalized welding bead image edges. IET Image Processing, 16(10), 2680–2696. https://doi.org/10.1049/ipr2.12517
Mukhiddinov, M., & Cho, J. (2021). Smart glass system using deep learning for the blind and visually impaired. Electronics (Switzerland), 10(22). https://doi.org/10.3390/electronics10222756
Mukhiddinov, M., & Kim, S. Y. (2021). A systematic literature review on the automatic creation of tactile graphics for the blind and visually impaired. Processes, 9(10), 1–31. https://doi.org/10.3390/pr9101726
Pakrasi, I., Laviers, A., & Chakraborty, N. (2018). A Design Methodology for Abstracting Character Archetypes onto Robotic Systems. Proceedings of International Conference on Movement Computing (MOCO’18). https://doi.org/10.1145/3212721.3212809
Qian, X., Liu, G., Wang, H., & Su, R. (2007). Text detection, localization, and tracking in compressed video. Signal Processing: Image Communication, 22(9), 752–768. https://doi.org/10.1016/j.image.2007.06.005
Rashid, S.F. (2014). Optical Character Recognition - A Combined ANN/HMM Approach.
Šarić, M. (2017). Scene text segmentation using low variation extremal regions and sorting based character grouping. Neurocomputing, 266, 56–65. https://doi.org/10.1016/j.neucom.2017.05.021
Seema Barate1, Chaitrali Kamthe2, Shweta Phadtare3, R. J. (2016). Implementasi Ekstraksi Karakter Teks dari Gambar Tulisan Tangan yang Dipotret ke Teknik Pencocokan Template Konversi Teks. MATEC Web of Conferences.
Soumya, B., & Hegde, S. S. (2018). Text Extraction in Images. 6(2), 119–121. https://www.ijcstjournal.org/volume-6/issue-2/IJCST-V6I2P24.pdf
Ye, Q., Huang, Q., Gao, W., & Zhao, D. (2005). Fast and robust text detection in images and video frames. Image and Vision Computing, 23(6), 565–576. https://doi.org/10.1016/j.imavis.2005.01.004
Zhang, H., Zhao, K., Song, Y. Z., & Guo, J. (2013). Text extraction from natural scene image: A survey. Neurocomputing, 122, 310–323. https://doi.org/10.1016/j.neucom.2013.05.037
Zhao, Q. J., Cao, P., & Meng, Q. X. (2014). Image Capturing and Segmentation Method for Characters Marked on Hot Billets. Advanced Materials Research, 945-949, 1830–1836. https://doi.org/10.4028/www.scientific.net/amr.945-949.1830
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Data Science
This work is licensed under a Creative Commons Attribution 4.0 International License.
