Price: $84.00 – $63.72
(as of Dec 25,2024 15:00:31 UTC – Details)
Publisher : LAP LAMBERT Academic Publishing (April 5, 2011)
Language : English
Paperback : 156 pages
ISBN-10 : 3844324615
ISBN-13 : 978-3844324617
Item Weight : 8.4 ounces
Dimensions : 5.91 x 0.36 x 8.66 inches
In today’s digital age, the ability to extract and understand text from images and videos has become increasingly important. Whether it’s for enhancing search capabilities, improving accessibility, or enabling new applications like augmented reality, text recognition technology is playing a crucial role in various industries.
Convolutional neural networks (CNNs) have been at the forefront of advancements in image and video text recognition. These deep learning models have shown remarkable success in tasks such as object detection, image classification, and semantic segmentation. In recent years, researchers have focused on developing new CNN architectures specifically tailored for binarization, segmentation, and recognition of text in images and videos.
In this study, we delve into the latest advancements in CNNs for text recognition and explore how these models can be optimized for handling text in various forms and languages. We investigate the challenges associated with binarization of text images, which involves converting grayscale or color images into binary images with clear text regions. We also look into segmentation techniques that help in isolating text regions from complex backgrounds, making it easier for recognition models to accurately identify and extract text.
Moreover, we discuss the importance of robust recognition models that can handle various fonts, sizes, and orientations of text. By training CNNs on large-scale text datasets, researchers have been able to achieve state-of-the-art performance in text recognition tasks, surpassing traditional OCR (optical character recognition) systems.
Overall, this study sheds light on the advancements in CNN architectures for text recognition and highlights the potential for further improvements in this field. As technology continues to evolve, we can expect even more sophisticated CNN models that can accurately and efficiently extract text from images and videos, opening up new possibilities for applications in areas such as document analysis, scene understanding, and visual search.
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