Character Recognition in Natural Scene Images Using Local Description

2012 ◽  
pp. 193-200
Author(s):  
Boyu Zhang ◽  
Wei Zhao ◽  
JiaFeng Liu ◽  
Rui Wu ◽  
XiangLong Tang
Keyword(s):  
Character Recognition ◽  
Natural Scene ◽  
Local Description ◽  
Natural Scene Images

Decorated Character Recognition Employing Modified SOM Matching

2011 ◽  
Vol 103 ◽  
pp. 649-657
Author(s):  
Tsukasa Masuhara ◽  
Hideaki Kawano ◽  
Hideaki Orii ◽  
Hiroshi Maeda
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Natural Scene ◽  
Input Interface ◽  
Annotation Method ◽  
Natural Scene Images

Character recognition is a classical issue which has been devoted by a lot of researchers.Making character recognition system more widely available in natural scene images might open upinteresting possibility to use as an input interface of characters and an annotation method for images.Nevertheless, it is still difficult to recognize all sorts of fonts including decorated characters such ascharacters depicted on signboards. The decorated characters are constructed by using some specialtechniques for attracting viewers' attentions. Therefore, it is hard to obtain good recognition results bythe existingOCRs. In this paper,we propose a newcharacter recognition systemusing SOM. The SOMis employed to extract an essential structure concerning the topology from a character. The extractedtopological structure from each character is used to matching and the recognition is performed on thebasis of the topological matching. Experimental results show the effectiveness of the proposed methodin most forms of characters.

scholarly journals PNN Based Character Recognition in Natural Scene Images

2016 ◽  
Vol 6 (Special Issue) ◽  
pp. 109-113 ◽  
Author(s):  
Shivananda V. Seeri ◽  
J.D. Pujari ◽  
P.S. Hiremath
Keyword(s):  
Character Recognition ◽  
Natural Scene ◽  
Natural Scene Images

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

Character recognition in natural scene images

2015 ◽  
Author(s):  
O. Akbani ◽  
A. Gokrani ◽  
M. Quresh ◽  
Furqan M. Khan ◽  
Sadaf I. Behlim ◽  
...  
Keyword(s):  
Character Recognition ◽  
Natural Scene ◽  
Natural Scene Images

A NOVEL METHOD FOR EXTRACTING TEXT FROM NATURAL SCENE IMAGES AND TTS

2019 ◽  
pp. 30-33
Author(s):  
U. R. Khamdamov ◽  
M. N. Mukhiddinov ◽  
A. O. Mukhamedaminov ◽  
O. N. Djuraev
Keyword(s):  
Natural Scene ◽  
Novel Method ◽  
Natural Scene Images

Ensemble Visual Content based Search and Retrieval for Natural Scene Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Template Matching ◽  
Feature Descriptor ◽  
Natural Scene ◽  
Visual Content ◽  
Other Information ◽  
Similar Images ◽  
State Of Art ◽  
Natural Scene Images ◽  
Hybrid Methodology ◽  
Search And Retrieval

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.

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.

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