Cognitively Inspired Video Text Processing
As technologies are fast advancing, the importance of text detection and recognition is receiving special attention from the researchers. Thus, one can see several real-time applications of video text processing which requires cognitive-based methods to find a solution. The main applications are (1) retrieving and indexing video based on semantic of the content of the video, (2) machine translation to assist foreigners, (3) assisting blind people to walk on the road freely without aid, (4) automatic vehicle driving, (5) license plate tracing to catch vehicles which violate the traffic signals, (6) monitoring the images posted on social media based on text and content of the images, (7) identifying the location based on the address of the street and shops, etc., (8) tracing players in the sports based on the jersey/bib number or text, and (9) in the same way, tracing the bib number in case of marathon and other events. For the above-mentioned applications, text detection and recognitionin video and natural scene images is an integral part of the system.
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Cognitively Inspired Video Text Processing
As technologies are fast advancing, the importance of text detection and recognition is receiving special attention from the researchers. Thus, one can see several real-time applications of video text processing which requires cognitive-based methods to find a solution. The main applications are (1) retrieving and indexing video based on semantic of the content of the video, (2) machine translation to assist foreigners, (3) assisting blind people to walk on the road freely without aid, (4) automatic vehicle driving, (5) license plate tracing to catch vehicles which violate the traffic signals, (6) monitoring the images posted on social media based on text and content of the images, (7) identifying the location based on the address of the street and shops, etc., (8) tracing players in the sports based on the jersey/bib number or text, and (9) in the same way, tracing the bib number in case of marathon and other events. For the above-mentioned applications, text detection and recognitionin video and natural scene images is an integral part of the system.
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Cognitively Inspired Video Text Processing

Cognitively Inspired Video Text Processing

by Palaiahnakote Shivakumara, Umapada Pal
Cognitively Inspired Video Text Processing

Cognitively Inspired Video Text Processing

by Palaiahnakote Shivakumara, Umapada Pal

Paperback(1st ed. 2021)

$199.99 
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Overview

As technologies are fast advancing, the importance of text detection and recognition is receiving special attention from the researchers. Thus, one can see several real-time applications of video text processing which requires cognitive-based methods to find a solution. The main applications are (1) retrieving and indexing video based on semantic of the content of the video, (2) machine translation to assist foreigners, (3) assisting blind people to walk on the road freely without aid, (4) automatic vehicle driving, (5) license plate tracing to catch vehicles which violate the traffic signals, (6) monitoring the images posted on social media based on text and content of the images, (7) identifying the location based on the address of the street and shops, etc., (8) tracing players in the sports based on the jersey/bib number or text, and (9) in the same way, tracing the bib number in case of marathon and other events. For the above-mentioned applications, text detection and recognitionin video and natural scene images is an integral part of the system.

Product Details

ISBN-13: 9789811670718
Publisher: Springer Nature Singapore
Publication date: 11/16/2021
Series: Cognitive Intelligence and Robotics
Edition description: 1st ed. 2021
Pages: 275
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of Contents

Chapter 1. Introduction.- Chapter 2. Key Text Frame Selection from Video.- Chapter 3. Text and Non-Text Frame Classification in Video.- Chapter 4. Text Detection in Video.- Chapter 5. Text Detection in Images.- Chapter 6. Word and Character Segmentation.- Chapter 7. Video Text Type Classification.- Chapter 8. Video Text Enhancement for Recognition.- Chapter 9. Video Text Recognition.- Chapter 10. Conclusion and Future Directions.
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