scholarly journals Comparative Study on Text Detection and Recognition from Traffic Image

2016 ◽  
Vol 2 (9) ◽  
Author(s):  
Asit Kumar ◽  
Sumit Kumar
Keyword(s):  
Neural Network ◽  
Texture Features ◽  
Text Detection ◽  
Natural Scenes ◽  
Connected Component ◽  
Extraction Techniques ◽  
Efficient Performance ◽  
Rich Information ◽  
Automatic Text ◽  
Detection And Recognition

Text plays an significant role in day-to-day life because of its dissimilarities in text size, font, style, orientation and alignment as well as composite background and rich information, as a consequence automatic text detection in natural scenes has several attractive applications. Though, detecting and recognizing such text is all the time a challenging issue. Several text extraction techniques grounded on edge detection, connected component analysis, morphological operators, wavelet transform, texture features, neural network etc. have been established. This paper contributes comparative analysis of different technique which provides efficient performance.

How Far Deep Learning Systems for Text Detection and Recognition in Natural Scenes are Affected by Occlusion?

2021 ◽  
pp. 198-212
Author(s):  
Aline Geovanna Soares ◽  
Byron Leite Dantas Bezerra ◽  
Estanislau Baptista Lima
Keyword(s):  
Deep Learning ◽  
Text Detection ◽  
Learning Systems ◽  
Natural Scenes ◽  
Detection And Recognition

scholarly journals Multilingual Text Detection with Nonlinear Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Lin Li ◽  
Shengsheng Yu ◽  
Luo Zhong ◽  
Xiaozhen Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Text Detection ◽  
Natural Scenes ◽  
Nonlinear Network ◽  
Text Feature ◽  
Representational Power ◽  
Multilingual Text

Multilingual text detection in natural scenes is still a challenging task in computer vision. In this paper, we apply an unsupervised learning algorithm to learn language-independent stroke feature and combine unsupervised stroke feature learning and automatically multilayer feature extraction to improve the representational power of text feature. We also develop a novel nonlinear network based on traditional Convolutional Neural Network that is able to detect multilingual text regions in the images. The proposed method is evaluated on standard benchmarks and multilingual dataset and demonstrates improvement over the previous work.

scholarly journals Research on Methods of English Text Detection and Recognition Based on Neural Network Detection Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chunlan Li
Keyword(s):  
Neural Network ◽  
Recognition Accuracy ◽  
Rapid Development ◽  
Research Work ◽  
Text Detection ◽  
English Text ◽  
Data Sets ◽  
Original Algorithm ◽  
Multiple Data Sets ◽  
Detection And Recognition

With the rapid development of computer science, a large number of images and an explosive amount of information make it difficult to filter and effectively extract information. This article focuses on the inability of effective detection and recognition of English text content to conduct research, which is useful for improving the application of intelligent analysis significance. This paper studies how to improve the neural network model to improve the efficiency of image text detection and recognition under complex background. The main research work is as follows: (1) An improved CTPN multidirectional text detection algorithm is proposed, and the algorithm is applied to the multidirectional text detection and recognition system. It uses the multiangle rotation of the image to be detected, then fuses the candidate text boxes detected by the CTPN network, and uses the fusion strategy to find the best area of the text. This algorithm solves the problem that the CTPN network can only detect the text in the approximate horizontal direction. (2) An improved CRNN text recognition algorithm is proposed. The algorithm is based on CRNN and combines traditional text features and depth features at the same time, making it possible to recognize occluded text. The algorithm was tested on the IC13 and SVT data sets. Compared with the CRNN algorithm, the recognition accuracy has been improved, and the detection and recognition accuracy has increased by 0.065. This paper verifies the effectiveness of the improved algorithm model on multiple data sets, which can effectively detect various English texts, and greatly improves the detection and recognition performance of the original algorithm.

scholarly journals Improvement of the end-to-end scene text recognition method for “text-to-speech” conversion

2020 ◽  
Vol 18 (06) ◽  
pp. 2050052
Author(s):  
Fazliddin Makhmudov ◽  
Mukhriddin Mukhiddinov ◽  
Akmalbek Abdusalomov ◽  
Kuldoshbay Avazov ◽  
Utkir Khamdamov ◽  
...  
Keyword(s):  
Text Detection ◽  
Text Recognition ◽  
Natural Scenes ◽  
Natural Scene ◽  
Text To Speech ◽  
Convolutional Network ◽  
Full Color ◽  
Active Research ◽  
Natural Scene Images ◽  
Detection And Recognition

Methods for text detection and recognition in images of natural scenes have become an active research topic in computer vision and have obtained encouraging achievements over several benchmarks. In this paper, we introduce a robust yet simple pipeline that produces accurate and fast text detection and recognition for the Uzbek language in natural scene images using a fully convolutional network and the Tesseract OCR engine. First, the text detection step quickly predicts text in random orientations in full-color images with a single fully convolutional neural network, discarding redundant intermediate stages. Then, the text recognition step recognizes the Uzbek language, including both the Latin and Cyrillic alphabets, using a trained Tesseract OCR engine. Finally, the recognized text can be pronounced using the Uzbek language text-to-speech synthesizer. The proposed method was tested on the ICDAR 2013, ICDAR 2015 and MSRA-TD500 datasets, and it showed an advantage in efficiently detecting and recognizing text from natural scene images for assisting the visually impaired.

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