Intellectual Curve Scene Text Detection from natural images using MSER descriptor based Region Segmentation approach

2019 ◽  
Vol 09 ◽  
Author(s):  
Deepak Kumar ◽  
Ramandeep SIngh
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Text Detection ◽  
Natural Images ◽  
Natural Image ◽  
Morphological Operations ◽  
Text Data ◽  
Region Detection ◽  
Scene Text Detection ◽  
Time Accuracy

A novel method to detect the text region from the natural image using the discriminative deep feature of text regions is presented with deep learning concept in this manuscript. Curve text detection (CTD) from the natural image is generally based on two different tasks: learning of text data and text region detection. In the learning of text data, the goal is to train the system with a sample of letters and natural images, while, in text region detection, the aim is to confirm the detected regions are text region or not. The emphasis of this research is on the development of deep learning algorithm. A novel approach has been proposed to detect the text region from natural images which simultaneously tackles three combined challenges: 1) pre-processing of the image without losing text region; 2) appropriate segmentation of text region using their strokes, and 3) training of data. In pre-processing, image enhancement and binarization are done then morphological operations are defined with the Maximally stable extremal region (MSER) based segmentation technique which operates on the basis of stroke region of text and then finds out the (Speed Up Robust Feature) SURF key point from those regions. Based on the SURF feature, text region is detected from the images using a trained structure of Artificial neural network (ANN) which is based on deep learning mechanism. CTW-1500 dataset is used to simulate the proposed work and the parameters like Precision, Recall, F-Measure (H-mean), Execution time, Accuracy and Error Rate are computed and are compared with the existing work to depict the effectiveness of the work.

Robust Scene Text Detection for Multi-script Languages Using Deep Learning

2016 ◽  
pp. 329-340
Author(s):  
Ruo-Ze Liu ◽  
Xin Sun ◽  
Hailiang Xu ◽  
Palaiahnakote Shivakumara ◽  
Feng Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Text Detection ◽  
Scene Text Detection ◽  
Scene Text

scholarly journals Deep Learning Based Truth Discovery Algorithm for Research the Genuineness of Given Text Corpus

2019 ◽  
Vol 8 (2S3) ◽  
pp. 605-611
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Multiple Sources ◽  
Text Data ◽  
Text Corpus ◽  
Automatic Feature Extraction ◽  
Deep Learning Algorithm ◽  
Truth Discovery ◽  
Improved Accuracy

Lot of research has gone into Natural language processing and the state of the art algorithms in deep learning that unambiguously helps in converting an English text into a data structure without loss of meaning. Also with the advent of neural networks for learning word representations as vectors has helped a lot in revolutionizing the automatic feature extraction from text data corpus. A combination of word embedding and the use of a deep learning algorithm like a convolution neural network helped in better accuracy for text classification. In this era of Internet of things and the voluminous amounts of data that is overwhelming the users determining the veracity of the data is a very challenging task. There are many truth discovery algorithms in literature that help in resolving the conflicts that arise due to multiple sources of data. These algorithms help in estimating the trustworthiness of the data and reliability of the sources. In this paper, a convolution based truth discovery with multitasking is proposed to estimate the genuineness of the data for a given text corpus. The proposed algorithm has been tested on analysing the genuineness of Quora questions dataset and experimental results showed an improved accuracy and speed over other existing approaches.

scholarly journals Deep learning for scene text detection and recognition

2018 ◽  
Vol 48 (5) ◽  
pp. 531-544 ◽  
Author(s):  
Xiang BAI ◽  
Minghui LIAO ◽  
Baoguang SHI ◽  
Mingkun YANG
Keyword(s):  
Deep Learning ◽  
Text Detection ◽  
Scene Text Detection ◽  
Scene Text ◽  
Detection And Recognition

scholarly journals Compact and Accurate Scene Text Detector

2020 ◽  
Vol 10 (6) ◽  
pp. 2096 ◽  
Author(s):  
Minjun Jeon ◽  
Young-Seob Jeong
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Text Detection ◽  
Floating Point ◽  
Learning Models ◽  
Proposed Model ◽  
Scene Text Detection ◽  
Scene Text

Scene text detection is the task of detecting word boxes in given images. The accuracy of text detection has been greatly elevated using deep learning models, especially convolutional neural networks. Previous studies commonly aimed at developing more accurate models, but their models became computationally heavy and worse in efficiency. In this paper, we propose a new efficient model for text detection. The proposed model, namely Compact and Accurate Scene Text detector (CAST), consists of MobileNetV2 as a backbone and balanced decoder. Unlike previous studies that used standard convolutional layers as a decoder, we carefully design a balanced decoder. Through experiments with three well-known datasets, we then demonstrated that the balanced decoder and the proposed CAST are efficient and effective. The CAST was about 1.1x worse in terms of the F1 score, but 30∼115x better in terms of floating-point operations per second (FLOPS).

scholarly journals Amrita-CEN-Senti-DB:Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

2020 ◽  
Author(s):  
vinayakumar R
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Support Vector ◽  
Text Data ◽  
Inverse Document Frequency ◽  
Logistics Regression ◽  
Document Frequency

<p><b>Social media is a platform in which tons and tons of text are generated each and every day. The data is so large that cannot be easily understood, so this has paved a path to a new field in the information technology which is natural language processing. In this paper, the text data which is used for the classification is tweets that determines the state of the person according of the sentiments which is positive, negative and neutral. Emotions are the way of expression of the person’s feelings which has a high influence on the decision making tasks. Here we have proposed the text representation, Term Frequency Inverse Document Frequency (tfidf), Keras embedding along with the machine learning and deep learning algorithms for the purpose of the classification of the sentiments, out of which Logistics Regression machine learning based methods out performs well when the features is taken in the limited amount as the features increases Support Vector Machine (SVM) which is also one of the machine learning algorithm out performs well making a benchmark accuracy for this dataset as the 75.8%. For the research purpose the dataset has been made publically available.</b><b></b></p>

Sign in / Sign up

Export Citation Format

Share Document