Text Region Detection and Recognition in Natural Scene Images Using MSER and Convolutional Neural Network

2020 ◽  
Author(s):  
Anantha Natarajan V ◽  
Sunil Kumar M ◽  
Tamizhazhagan V
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Scene ◽  
Region Detection ◽  
Natural Scene Images ◽  
Detection And Recognition

scholarly journals Cursive Character Recognition in Natural Scene Images Using a Multilevel Convolutional Neural Network Fusion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 109054-109070
Author(s):  
Asghar Ali Chandio ◽  
Md. Asikuzzaman ◽  
Mark R. Pickering
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Natural Scene ◽  
Natural Scene Images

CRF based text detection for natural scene images using convolutional neural network and context information

2018 ◽  
Vol 295 ◽  
pp. 46-58 ◽  
Author(s):  
Yanna Wang ◽  
Cunzhao Shi ◽  
Baihua Xiao ◽  
Chunheng Wang ◽  
Chengzuo Qi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Text Detection ◽  
Context Information ◽  
Natural Scene ◽  
Natural Scene Images

scholarly journals Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1804
Author(s):  
Wentai Lei ◽  
Jiabin Luo ◽  
Feifei Hou ◽  
Long Xu ◽  
Ruiqing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Vital Role ◽  
Short Term ◽  
Term Memory ◽  
Region Detection ◽  
Field Environment ◽  
Long Short Term Memory ◽  
Lstm Network

Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. However, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets.

scholarly journals Urdu-Text Detection and Recognition in Natural Scene Images Using Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96787-96803 ◽  
Author(s):  
Syed Yasser Arafat ◽  
Muhammad Javed Iqbal
Keyword(s):  
Deep Learning ◽  
Text Detection ◽  
Natural Scene ◽  
Natural Scene Images ◽  
Detection And Recognition
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