Devanagari Text Detection From Natural Scene Images

2020 ◽  
Vol 10 (3) ◽  
pp. 44-59
Author(s):  
Sankirti Sandeep Shiravale ◽  
R. Jayadevan ◽  
Sanjeev S. Sannakki
Keyword(s):  
Edge Detection ◽  
Image Understanding ◽  
Text Detection ◽  
Experimental Results ◽  
Combined Approach ◽  
Natural Scene ◽  
Light Conditions ◽  
The Individual ◽  
Natural Scene Images ◽  
Better Than

Text present in a camera captured scene images is semantically rich and can be used for image understanding. Automatic detection, extraction, and recognition of text are crucial in image understanding applications. Text detection from natural scene images is a tedious task due to complex background, uneven light conditions, multi-coloured and multi-sized font. Two techniques, namely ‘edge detection' and ‘colour-based clustering', are combined in this paper to detect text in scene images. Region properties are used for elimination of falsely generated annotations. A dataset of 1250 images is created and used for experimentation. Experimental results show that the combined approach performs better than the individual approaches.

Mining discriminative patches for script identification in natural scene images

2021 ◽  
Vol 40 (1) ◽  
pp. 551-563
Author(s):  
Liqiong Lu ◽  
Dong Wu ◽  
Ziwei Tang ◽  
Yaohua Yi ◽  
Faliang Huang
Keyword(s):  
Neural Networks ◽  
Experimental Results ◽  
The Other ◽  
Natural Scene ◽  
Fixed Size ◽  
Script Identification ◽  
Aspect Ratios ◽  
Novel Approach ◽  
Public Datasets ◽  
Natural Scene Images

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.

A robust arbitrary text detection system for natural scene images

2014 ◽  
Vol 41 (18) ◽  
pp. 8027-8048 ◽  
Author(s):  
Anhar Risnumawan ◽  
Palaiahankote Shivakumara ◽  
Chee Seng Chan ◽  
Chew Lim Tan
Keyword(s):  
Detection System ◽  
Text Detection ◽  
Natural Scene ◽  
Natural Scene Images

Multilingual Scene Text Detection Using Gradient Morphology

2020 ◽  
Vol 10 (3) ◽  
pp. 31-43 ◽  
Author(s):  
Dibyajyoti Dhar ◽  
Neelotpal Chakraborty ◽  
Sayan Choudhury ◽  
Ashis Paul ◽  
Ayatullah Faruk Mollah ◽  
...  
Keyword(s):  
State Of The Art ◽  
High Sensitivity ◽  
Text Detection ◽  
Interesting Problem ◽  
Natural Scene ◽  
The Past ◽  
Scene Text Detection ◽  
Scene Text ◽  
Multiple Languages ◽  
Natural Scene Images

Text detection in natural scene images is an interesting problem in the field of information retrieval. Several methods have been proposed over the past few decades for scene text detection. However, the robustness and efficiency of these methods are downgraded due to high sensitivity towards various complexities of an image. Also, in multi-lingual environment where texts may occur in multiple languages, a method may not be suitable for detecting scene texts in certain languages. To counter these challenges, a gradient morphology-based method is proposed in this paper that proves to be robust against image complexities and efficiently detects scene texts irrespective of their languages. The method is validated using low quality images from standard multi-lingual datasets like MSRA-TD500 and MLe2e. The performance of the method is compared with that of some state-of-the-art methods, and comparably better results are observed.

Text detection in natural scene images with user-intention

2013 ◽  
Author(s):  
Liuan Wang ◽  
Yutaka Katsuyama ◽  
Wei Fan ◽  
Yuan He ◽  
Jun Sun ◽  
...  
Keyword(s):  
Text Detection ◽  
Natural Scene ◽  
User Intention ◽  
Natural Scene Images

Arbitrarily-Oriented Text Detection in Low Light Natural Scene Images

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Minglong Xue ◽  
Palaiahnakote Shivakumara ◽  
Chao Zhang ◽  
Yao Xiao ◽  
Tong Lu ◽  
...  
Keyword(s):  
Text Detection ◽  
Natural Scene ◽  
Low Light ◽  
Natural Scene Images
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