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Scene Text Extraction using Convolutional Neural Network with Amended MSER

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Title Scene Text Extraction using Convolutional Neural Network with Amended MSER
 
Creator Yegnaraman, A
Valli, S
 
Subject Convolution layer
Deep learning framework
Focal loss
Maximally stable extremal regions
YOLOv2
 
Description 817-827
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 byevaluating three publicly available datasets. The method a-MSER is used to identify the region of interest based on thevariation of MSER. This algorithm considers intensity changes between text and background very effectively. The drawbackof original YOLOv2, the poor detection rate for small-sized objects, is overcome by employing 1 × 1 layer with image sizeenhanced from 13 × 13 to 26 × 26. Focal loss is applied to improve upon the existing cross entropy classification loss ofYOLOv2. The repeated convolution layer in the steep layer of the original YOLOv2 is removed to reduce the networkcomplexity as it does not improve the system performance. Experimental results demonstrate that the proposed method isproductive in identifying text from natural scene images.
 
Date 2021-09-24T05:15:01Z
2021-09-24T05:15:01Z
2021-09
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58136
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(09) [September 2021]