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<span lang="EN-US">Scene Text Extraction using Convolutional Neural Network with Amended MSER</span><span> </span>

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Title Statement <span lang="EN-US">Scene Text Extraction using Convolutional Neural Network with Amended MSER</span><span> </span>
 
Added Entry - Uncontrolled Name Yegnaraman, Aparna ; Department of Computer Science and Engineering, College of Engineering, Guindy, Anna University, Chennai 600 025, TN, India
Valli, S ; Department of Computer Science and Engineering, College of Engineering, Guindy, Anna University, Chennai 600 025, TN, India
Department of Science and Technology, New Delhi, India
 
Uncontrolled Index Term Convolution layer, Deep learning framework, Focal loss, Maximally stable extremal regions, YOLOv2
 
Summary, etc. <p style="text-align: justify;">Content in the text format helps to communicate the relevant and specific information to users meticulously. A beneficial approach for extracting text from natural scene images is introduced which employs amended Maximally Stable Extremal Region (a-MSER) together with deep learning framework, You Only Look Once YOLOv2 network. The proposed system, a-MSER with Scene Text Extraction using Modified YOLOv2 Network (STEMYN), performs remarkably well by evaluating three publicly available datasets. The method a-MSER is used to identify the region of interest based on the variation of MSER. This algorithm considers intensity changes between text and background very effectively. The drawback of original YOLOv2, the poor detection rate for small-sized objects, is overcome by employing 1 × 1 layer with image size enhanced from 13 × 13 to 26 × 26. Focal loss is applied to improve upon the existing cross entropy classification loss of YOLOv2. The repeated convolution layer in the steep layer of the original YOLOv2 is removed to reduce the network complexity as it does not improve the system performance. Experimental results demonstrate that the proposed method is productive in identifying text from natural scene images.</p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2021-10-29 12:28:08
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/46089
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 80, ##issue.no## 09 (2021): Journal of Scientific and Industrial Research
 
Language Note en