DetReco: Object-Text Detection and Recognition Based on Deep Neural Network

Zhai, Fan; Luan, Jiaxing; Xu, Zhichao; Chen, Wei

doi:10.1155/2020/2365076

Cited by 13 publications

(8 citation statements)

References 41 publications

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“…The subscripts resemble the initials of what every matrix denotes (i.e., O W is the hidden forget weight matrix). Moreover, :, 9, , and signify the forget, input, output, and cell gate vectors [19]. Therefore, the LSTM technique is mostly related to the RNN method but with LSTM cells' inclusion.…”

Section: B Phase Ii: Text Recognition Processmentioning

confidence: 99%

Intelligent Deep Learning Empowered Text Detection Model from Natural Scene Images

Devi¹,

CN²

2022

International Journal on Advanced Science, Engineering and Information Technology

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The scene Text Recognition process has become a hot research topic and a challenging task owing to the complicated background, varying light intensities, colors, font styles, and sizes. Text extraction from natural scene images encompasses two main processes: text detection and text recognition. The latest advancements in Machine Learning (ML) and Deep Learning (DL) concepts can effectually automate the text detection and recognition process by training the model properly. In this view, this paper presents an Automated DL empowered Text Detection model from Natural Scene Images (ADLTD-NSI). The ADLTD-NSI technique includes two important processes: text detection and text recognition. Firstly, a single shot detector (SSD) with Inception-v2 as a baseline model is employed for text detection, an object detector based on the VGG-16 framework for feature map extraction followed by six convolution layers. Secondly, Convolutional Recurrent Neural Network (CRNN) technique is utilized for the text recognition process. Besides, the recurrent layers in the CRNN model utilize long short-term memory (LSTM) for encoding the sequence of feature vectors. Lastly, Connectionist Temporal Classification (CTC) loss is applied to predict text labels equivalent to the sequences from the recurrent layers. A wide range of experiments was carried out on benchmark COCO datasets, and the results are examined in several aspects. The experimental outcomes showcased the better performance of the ADLTD-NSI technique over the other compared methods with a maximum accuracy of 96.78%.

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Section: B Phase Ii: Text Recognition Processmentioning

confidence: 99%

Intelligent Deep Learning Empowered Text Detection Model from Natural Scene Images

Devi¹,

CN²

2022

International Journal on Advanced Science, Engineering and Information Technology

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“…This method uses k-means clustering for the above features to extract the text candidates. Zhang et al [30] propose a scheme that combines both object text-detection (used YOLOv3) and text recognition (employed with CRNN-Convolutional Recurrent Neural Network). Based on the above findings, it is clear that detecting text in videos is extremely challenging.…”

Section: Text Detectionmentioning

confidence: 99%

DEFUSE: Deep Fused End-to-End Video Text Detection and Recognition

Lokkondra¹,

Ramegowda²,

Thimmaiah³

et al. 2022

RIA

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Detecting and recognizing text in natural scene videos and images has brought more attention to computer vision researchers due to applications like robotic navigation and traffic sign detection. In addition, Optical Character Recognition (OCR) technology is applied to detect and recognize text on the license plate. It will be used in various commercial applications such as finding stolen cars, calculating parking fees, invoicing tolls, or controlling access to safety zones and aids in detecting fraud and secure data transactions in the banking industry. Much effort is required when scene text videos are in low contrast and motion blur with arbitrary orientations. Presently, text detection and recognition approaches are limited to static images like horizontal or approximately horizontal text. Detecting and recognizing text in videos with data dynamicity is more challenging because of the presence of multiple blurs caused by defocusing, motion, illumination changes, arbitrarily shaped, and occlusion. Thus, we proposed a combined DeepEAST (Deep Efficient and Accurate Scene Text Detector) and Keras OCR model to overcome these challenges in the proffered DEFUSE (Deep Fused) work. This two-combined technique detects the text regions and then deciphers the result into a machine-readable format. The proposed method has experimented with three different video datasets such as ICDAR 2015, Road Text 1K, and own video Datasets. Our results proved to be more effective with precision, recall, and F1-Score.

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“…Figure 1 shows different computer vision technique which are used in different domain of smart transportation. Smart transportation is not limited to listed domain, there is also minor domains like government rules for transportation [30], vehicle support facility, transportation payment facilities…”

Section: Smart Transportationmentioning

confidence: 99%

The Convergence of Deep Learning and Computer Vision: Smart City Applications and Research Challenges

Kothadiya¹,

Chaudhari²,

Macwan³

et al. 2021

Atlantis Highlights in Computer Sciences

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In recent years, deep learning strategies started to outshine traditional machine learning methods in a few fields, with Computer Vision being one of the most noticeable ones. The Computer Vision is becoming more suitable nowadays at identifying patterns from images than the human visual cognitive system. It ranges from raw information recording to methods and ideas that span digital image processing, machine learning, and computer graphics. The wide utilization of Computer Vision has attracted many researchers to incorporate their ideas with different fields and disciplines. The era of smart cities has emerged to meet the recent demands of citizens using information and communication technology. This paper reviews research efforts that utilize Deep Learning Frameworks and Computer Vision Applications in support of smart city applications like smart healthcare, smart transportation, smart agriculture, etc. Furthermore, the paper identified key research challenges that emanate from the use of deep learning and computer vision in support of smart city services.

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DetReco: Object-Text Detection and Recognition Based on Deep Neural Network

Cited by 13 publications

References 41 publications

Intelligent Deep Learning Empowered Text Detection Model from Natural Scene Images

Intelligent Deep Learning Empowered Text Detection Model from Natural Scene Images

DEFUSE: Deep Fused End-to-End Video Text Detection and Recognition

The Convergence of Deep Learning and Computer Vision: Smart City Applications and Research Challenges

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