A Multi-Object Rectified Attention Network for Scene Text Recognition

Luo, Canjie; Jin, Lianwen; Sun, Zenghui

doi:10.48550/arxiv.1901.03003

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…L. Zhang [12]: suggest a reliable framework for recognising licence plates in the wild. It is made up of an elaborately built image-to-sequence network for plate recognition and a customised CycleGAN model for creating licence plate images.…”

Section: Literature Surveymentioning

confidence: 99%

Number Plate Detection in an Image

Yamini

2023

IJSREM

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Automatic Vehicle license plate detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for license plate detection and recognitions. Due to the varying characteristics of the license plate like numbering system, colors, style and sizes of license plate, When detection and recognition are two separate jobs, which also results in a huge number of factors, there is an issue with identification. So,further research is still needed in this area. We propose a unified convolutional neural network (CNN) and the F1 score as metrics in a deep learning project for picture categorization which can localize license plates and recognize the letters. We work on license plate recognition and segments characters in the license plate firstly, and then recognizes each segmented character using Optical Character Recognition(OCR)techniques. Extensive experiments show the effectiveness and the efficiency of our proposed approach.

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Section: Literature Surveymentioning

confidence: 99%

Number Plate Detection in an Image

Yamini

2023

IJSREM

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“…Since we compare the spatial distance as a metric, the recognizer is not affected by any form of ID losses. Shi et al [40], for example, used the CTC loss [41] for convolutional recurrent neural networks, and attentional decoders [42,43] are guided by the cross-entropy loss. As a result, our framework is amenable to various recognizers.…”

Section: Recognizermentioning

confidence: 99%

Learning More in Vehicle Re-Identification: Joint Local Blur Transformation and Adversarial Network Optimization

Chen

Sheng

et al. 2022

Applied Sciences

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Vehicle re-identification (ReID) tasks are an important part of smart cities and are widely used in public security. It is extremely challenging because vehicles with different identities are generated from a uniform pipeline and cannot be distinguished based only on the subtle differences in their characteristics. To enhance the network’s ability to handle the diversity of samples in order to adapt to the changing external environment, we propose a novel data augmentation method to improve its performance. Our deep learning framework mainly consists of a local blur transformation and a transformation adversarial module. In particular, we first use a random selection algorithm to find a local region of interest in an image sample. Then, the parameter generator network, a lightweight convolutional neural network, is responsible for generating four weights and then as a basis to form a filter matrix for further blur transformations. Finally, an adversarial module is employed to ensure that as much noise information as possible is added to the image sample while preserving the structure of the training datasets. Furthermore, by updating the parameter generator network, the adversarial module can help produce more appropriate and harder training samples and lead to improving the framework’s performance. Extensive experiments on datasets, i.e., VeRi-776, VehicleID, and VERI-Wild, show that our method is superior to the state-of-the-art methods.

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“…To identify participants' usernames, we utilized a publicly available scene text recognition library. 13 The library uses a text detection approach that is based on EAST [41] for detecting text location and MORAN [42] for subsequently recognizing the composing characters. To filter out words that are not username candidates, we used a list of Zoom meeting-related words.…”

Section: Feature Extraction By Image Processing Of Collagesmentioning

confidence: 99%

Zooming Into Video Conferencing Privacy and Security Threats

Kagan¹,

Alpert²,

Fire³

2020

Preprint

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The COVID-19 pandemic outbreak, with its related social distancing and shelter-in-place measures, has dramatically affected ways in which people communicate with each other, forcing people to find new ways to collaborate, study, celebrate special occasions, and meet with family and friends. One of the most popular solutions that have emerged is the use of video conferencing applications to replace face-to-face meetings with virtual meetings. This resulted in unprecedented growth in the number of video conferencing users. In this study, we explored privacy issues that may be at risk by attending virtual meetings. We extracted private information from collage images of meeting participants that are publicly posted on the Web. We used image processing, text recognition tools, as well as social network analysis to explore our web crawling curated dataset of over 15,700 collage images, and over 142,000 face images of meeting participants. We demonstrate that video conference users are facing prevalent security and privacy threats. Our results indicate that it is relatively easy to collect thousands of publicly available images of video conference meetings and extract personal information about the participants, including their face images, age, gender, usernames, and sometimes even full names. This type of extracted data can vastly and easily jeopardize people's security and privacy both in the online and real-world, affecting not only adults but also more vulnerable segments of society, such as young children and older adults. Finally, we show that cross-referencing facial image data with social network data may put participants at additional privacy risks they may not be aware of and that it is possible to identify users that appear in several video conference meetings, thus providing a potential to maliciously aggregate different sources of information about a target individual.

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A Multi-Object Rectified Attention Network for Scene Text Recognition

Cited by 3 publications

References 41 publications

Number Plate Detection in an Image

Number Plate Detection in an Image

Learning More in Vehicle Re-Identification: Joint Local Blur Transformation and Adversarial Network Optimization

Zooming Into Video Conferencing Privacy and Security Threats

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