Fine-Grained Language Identification in Scene Text Images

2021 ◽  
Author(s):  
Yongrui Li ◽  
Shilian Wu ◽  
Jun Yu ◽  
Zengfu Wang
Keyword(s):  
Language Identification ◽  
Fine Grained ◽  
Scene Text ◽  
Text Images

Language identification from multi-lingual scene text images: a CNN based classifier ensemble approach

2020 ◽  
Author(s):  
Neelotpal Chakraborty ◽  
Soumyadeep Kundu ◽  
Sayantan Paul ◽  
Ayatullah Faruk Mollah ◽  
Subhadip Basu ◽  
...  
Keyword(s):  
Language Identification ◽  
Classifier Ensemble ◽  
Ensemble Approach ◽  
Scene Text ◽  
Text Images

scholarly journals A Robot Object Recognition Method Based on Scene Text Reading in Home Environments

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1919
Author(s):  
Shuhua Liu ◽  
Huixin Xu ◽  
Qi Li ◽  
Fei Zhang ◽  
Kun Hou
Keyword(s):  
Object Recognition ◽  
Recognition Accuracy ◽  
Multiple Objects ◽  
Recognition Method ◽  
Home Environments ◽  
Complex Scenes ◽  
Scene Text ◽  
Text Reading ◽  
Text Images ◽  
Chinese And English

With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning models with high accuracy are adopted to detect and recognize text in multi-view. Second, datasets including 102,000 Chinese and English scene text images and their inverse are generated. The F-measure of text detection is improved by 0.4% and the recognition accuracy is improved by 1.26% because the model is trained by these two datasets. Finally, a robot object recognition method is proposed based on the scene text reading. The robot detects and recognizes texts in the image and then stores the recognition results in a text file. When the user gives the robot a fetching instruction, the robot searches for corresponding keywords from the text files and achieves the confidence of multiple objects in the scene image. Then, the object with the maximum confidence is selected as the target. The results show that the robot can accurately distinguish objects with arbitrary shape and category, and it can effectively solve the problem of object recognition in home environments.

Efficient Character Skew Rectification in Scene Text Images

2015 ◽  
pp. 134-146
Author(s):  
Michal Bušta ◽  
Tomáš Drtina ◽  
David Helekal ◽  
Lukáš Neumann ◽  
Jiří Matas
Keyword(s):  
Scene Text ◽  
Text Images

A novel method for binarization of scene text images and its application in text identification

2018 ◽  
Vol 22 (4) ◽  
pp. 1361-1375 ◽  
Author(s):  
Ranjit Ghoshal ◽  
Anandarup Roy ◽  
Ayan Banerjee ◽  
Bibhas Chandra Dhara ◽  
Swapan K. Parui
Keyword(s):  
Scene Text ◽  
Novel Method ◽  
Text Images

scholarly journals SynthText3D: synthesizing scene text images from 3D virtual worlds

2020 ◽  
Vol 63 (2) ◽  
Author(s):  
Minghui Liao ◽  
Boyu Song ◽  
Shangbang Long ◽  
Minghang He ◽  
Cong Yao ◽  
...  
Keyword(s):  
Virtual Worlds ◽  
3D Virtual Worlds ◽  
Scene Text ◽  
Text Images

Multi-lingual scene text detection and language identification

2020 ◽  
Vol 138 ◽  
pp. 16-22 ◽  
Author(s):  
Shaswata Saha ◽  
Neelotpal Chakraborty ◽  
Soumyadeep Kundu ◽  
Sayantan Paul ◽  
Ayatullah Faruk Mollah ◽  
...  
Keyword(s):  
Text Detection ◽  
Language Identification ◽  
Scene Text Detection ◽  
Scene Text

scholarly journals Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

2019 ◽  
Vol 33 ◽  
pp. 8610-8617 ◽  
Author(s):  
Hui Li ◽  
Peng Wang ◽  
Chunhua Shen ◽  
Guyu Zhang
Keyword(s):  
State Of The Art ◽  
Text Recognition ◽  
Natural Scene ◽  
Fine Grained ◽  
Word Level ◽  
Scene Text ◽  
Sophisticated Model ◽  
Regular Text ◽  
Algorithm Implementation ◽  
Scene Text Recognition

Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion. Most existing approaches rely heavily on sophisticated model designs and/or extra fine-grained annotations, which, to some extent, increase the difficulty in algorithm implementation and data collection. In this work, we propose an easy-to-implement strong baseline for irregular scene text recognition, using offthe-shelf neural network components and only word-level annotations. It is composed of a 31-layer ResNet, an LSTMbased encoder-decoder framework and a 2-dimensional attention module. Despite its simplicity, the proposed method is robust. It achieves state-of-the-art performance on irregular text recognition benchmarks and comparable results on regular text datasets. The code will be released.

scholarly journals Reduced Annotation Based on Deep Active Learning for Arabic Text Detection in Natural Scene Images

2021 ◽  
Author(s):  
Khalil Boukthir ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
habib dhahri ◽  
Adel Alimi
Keyword(s):  
Active Learning ◽  
Text Detection ◽  
Training Data ◽  
Arabic Text ◽  
Natural Scene ◽  
Novel Approach ◽  
Training Samples ◽  
Scene Text ◽  
Text Images ◽  
Natural Scene Images

<div>- A novel approach is presented to reduced annotation based on Deep Active Learning for Arabic text detection in Natural Scene Images.</div><div>- A new Arabic text images dataset (7k images) using the Google Street View service named TSVD.</div><div>- A new semi-automatic method for generating natural scene text images from the streets.</div><div>- Training samples is reduced to 1/5 of the original training size on average.</div><div>- Much less training data to achieve better dice index : 0.84</div>

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