Cite this DOI

B.Avinash, Shaik.Ishrath Anjum, Syed.Roohi, Vibudi.Divya Priya, & Venicharla.Bhargavi (2024). Real -Time Text Detection and Recognition Based on Optical Character Recognition(OCR). *Journal of Science & Technology*, *09*(04), 1. https://doi.org/10.46243/jst.2024.v9.i4.pp1-10

@article{bavinash2024real,
  author    = {B.Avinash and Shaik.Ishrath Anjum and Syed.Roohi and Vibudi.Divya Priya and Venicharla.Bhargavi},
  title     = {{Real -Time Text Detection and Recognition Based on Optical Character Recognition(OCR)}},
  journal   = {Journal of Science \& Technology},
  year      = {2024},
  month     = {apr},
  volume    = {09},
  number    = {04},
  pages     = {1},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2024.v9.i4.pp1-10},
  url       = {https://doi.org/10.46243/jst.2024.v9.i4.pp1-10},
  language  = {en},
  abstract  = {Text presented in videos includes vital information for content analysis, indexing, andretrieval of videos. Finding,verifying, and recognizing video text against complex backgroundsis a key technique for extracting this content. The existing system faces challenges ineffectively detecting and recognizing text content in images, limiting its applications to only images and recognizing text in English. This highlights the limitations of the CTPN network, which can only detect text in approximate horizontal directions. Additionally, the CRNN algorithm used for text recognition lacks efficiencyin handling occluded text, indicating the need for improvement in both text detection and recognitionunder complex backgrounds.Our proposed system suggests a method for text detection and recognition in real-time videos, and web cameras with enhanced multilingual support and converts any language text into English.Efficient handling of occluded text, Efficient handling of text orientations, variations of different fontstyles, sizes and text distortions using text detection tools such as the OpenCV. This tool extracts the region of interest of text in the video frames, implementing text detection throughOptical Character Recognition (OCR) and Pytesseract. OCR and Pytesseract extract text from video frames and pre-process frames for better recognition, enabling automated text extraction and analysis in videos. This approach offers a promising solution with much more better results than the existing method for detecting text in videos and web cameras.}
}

TY  - JOUR
TI  - Real -Time Text Detection and Recognition Based on Optical Character Recognition(OCR)
AU  - B.Avinash
AU  - Shaik.Ishrath Anjum
AU  - Syed.Roohi
AU  - Vibudi.Divya Priya
AU  - Venicharla.Bhargavi
JO  - Journal of Science & Technology
PY  - 2024
DA  - 2024/04/01/
VL  - 09
IS  - 04
SP  - 1
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Text presented in videos includes vital information for content analysis, indexing, andretrieval of videos. Finding,verifying, and recognizing video text against complex backgroundsis a key technique for extracting this content. The existing system faces challenges ineffectively detecting and recognizing text content in images, limiting its applications to only images and recognizing text in English. This highlights the limitations of the CTPN network, which can only detect text in approximate horizontal directions. Additionally, the CRNN algorithm used for text recognition lacks efficiencyin handling occluded text, indicating the need for improvement in both text detection and recognitionunder complex backgrounds.Our proposed system suggests a method for text detection and recognition in real-time videos, and web cameras with enhanced multilingual support and converts any language text into English.Efficient handling of occluded text, Efficient handling of text orientations, variations of different fontstyles, sizes and text distortions using text detection tools such as the OpenCV. This tool extracts the region of interest of text in the video frames, implementing text detection throughOptical Character Recognition (OCR) and Pytesseract. OCR and Pytesseract extract text from video frames and pre-process frames for better recognition, enabling automated text extraction and analysis in videos. This approach offers a promising solution with much more better results than the existing method for detecting text in videos and web cameras.
DO  - 10.46243/jst.2024.v9.i4.pp1-10
UR  - https://doi.org/10.46243/jst.2024.v9.i4.pp1-10
ER  -

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    "DOI": "10.46243/jst.2024.v9.i4.pp1-10",
    "URL": "https://doi.org/10.46243/jst.2024.v9.i4.pp1-10",
    "title": "Real -Time Text Detection and Recognition Based on Optical Character Recognition(OCR)",
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    "author": [
        {
            "family": "B.Avinash"
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        {
            "family": "Shaik.Ishrath Anjum"
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            "family": "Syed.Roohi"
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            "family": "Vibudi.Divya Priya"
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    "language": "en",
    "abstract": "Text presented in videos includes vital information for content analysis, indexing, andretrieval of videos. Finding,verifying, and recognizing video text against complex backgroundsis a key technique for extracting this content. The existing system faces challenges ineffectively detecting and recognizing text content in images, limiting its applications to only images and recognizing text in English. This highlights the limitations of the CTPN network, which can only detect text in approximate horizontal directions. Additionally, the CRNN algorithm used for text recognition lacks efficiencyin handling occluded text, indicating the need for improvement in both text detection and recognitionunder complex backgrounds.Our proposed system suggests a method for text detection and recognition in real-time videos, and web cameras with enhanced multilingual support and converts any language text into English.Efficient handling of occluded text, Efficient handling of text orientations, variations of different fontstyles, sizes and text distortions using text detection tools such as the OpenCV. This tool extracts the region of interest of text in the video frames, implementing text detection throughOptical Character Recognition (OCR) and Pytesseract. OCR and Pytesseract extract text from video frames and pre-process frames for better recognition, enabling automated text extraction and analysis in videos. This approach offers a promising solution with much more better results than the existing method for detecting text in videos and web cameras.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2024.v9.i4.pp1-10 gives all four in one JSON answer.