Multi-Lingual Scene Text Detection Using One-Class Classifier

Mukhopadhyay, Anirban; Kumar, Sourav; Chowdhury, Souvik Roy; Chakraborty, Neelotpal; Mollah, Ayatullah Faruk; Basu, Subhadip; Sarkar, Ram

doi:10.4018/ijcvip.2019040104

Cited by 19 publications

(3 citation statements)

References 31 publications

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“…The method [31] applies CNN for classification and thus requires high computation time for evaluating the training model compared to proposed method using traditional classifiers. Mukhopadhyay et al [32] used 100 images with one-class classifier & obtained 71% accuracy, whereas we acquired (83%) obtained in our work.…”

Section: Experiments and Resultsmentioning

confidence: 55%

Building Machine Learning Models for Classification of Text and Non-text Elements in Natural Scene Images

Soni¹,

Sharma

2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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Computer vision aims to build autonomous systems that can perform some of the human visual system’s tasks (and even surpass it in many cases)among the several applications of Computer Vision, extracting the information from the natural scene images is famous and influential. The information gained from an image can vary from identification, space measurements for navigation, or augmented reality applications. These scene images contain relevant text elements as well as many non-text elements. Prior to extracting meaningful information from the text, the foremost task is to classify the text & non-text elements correctly in the given images. The present paper aims to build machine learning models for accurately organizing the text and non-text elements in the benchmark dataset ICDAR 2013. The result is obtained in terms of the confusion matrix to determine the overall accuracy of the different machine learning models.

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Section: Experiments and Resultsmentioning

confidence: 55%

Building Machine Learning Models for Classification of Text and Non-text Elements in Natural Scene Images

Soni¹,

Sharma

2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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“…The approach utilised by Dutta et al [44] implements the relatively new concept of two-level grey level binding. The approach suggested in [45] seeks to dynamically set the percentage restriction of intensity variation and select the appropriate region area range for MSER to extract text regions from an image.…”

Section: Related Studymentioning

confidence: 99%

An Improved Scene Text Detection approach by utilising a Dual Discriminator framework

Pattanaik

Balabantaray

2023

Preprint

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Deep learning’s emergence and continued advancement have significantlychanged and redefined computer vision. Scene text identification andrecognition, a significant area of computer vision research, has unavoidablybeen impacted by this wave of innovation, and consequently hasentered the era of deep learning.It has a wide range of practical uses,from semantic natural scene analysis to navigation for those with visualimpairments. The complexity of the background, the image quality, thetext orientation, the text size, etc. present obstacles to experts in thefield of scene text analysis. Our proposed model provides a solution forscene text localization, detection, and refinement by integrating featurebasedand deep learning-based approaches. To obtain high quality andclear images, our model super-resolves images and deblurs them usingan Edge Attention Network (EAN). It is followed by a candidate regionproposal module that uses Maximally Stable Extremal Region (MSER)and Stroke Width Transform (SWT) to create text region suggestions.The detection result is then improved using a post-processing methodthat uses the idea of a Generative Adversarial Network (GAN). Standarddatasets including ICDAR 2013, ICDAR 2015, and the Street View Text(SVT) have been used for experiments verification which concludes thatour proposed model consistently produces acceptable results.

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“…They have reported maximum F-measure as 0.975 for Kannada and Malayalam with PCA, with feature size 60 and is large. Anirban Mukhopadhyay et al [1], have attempted the problem of scene text detection in the multi-lingual scenario by using the novel one-class classifier(OCC). They have considered the English, Hindi, and Bangla scripts.…”

Section: Literature Surveymentioning

confidence: 99%

Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features

Dhandra¹,

Mallappa²,

Mukarambi³

2019

IJRTE

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 Abstract: Most of the documents in various application areas like Government, Business and Research are available in the form of bi-lingual/multi-lingual text document. The multilingual documents are captured from video/camera for identification of script of the text document for automatic reading and editing. In this paper, an attempt is made to address the problem of script identification from camera captured document images using SFTA features. The input image is decomposed into a group of binary images by applying TTBD with fixing the number of the threshold as t n =3 empirically, on each decomposed binary image, Box Count, Mean Gray Level, and Pixel Count are extracted to form the feature vector. This feature vector is submitted to K-NN classifier to identify the scripts of the input document image. In all 10 scripts of the Indian languages are considered along with common English language as bi-lingual documents. The novelty of the paper is that 7 features are selected as potential features to obtain the highest accuracy. Features like Box Count (3), Mean Gray Level(2), and Pixel Count (2) have obtained the 87.02% recognition accuracy for English and Hindi Script combinations for the collected dataset and encouraging results for other combinations. These 7 potential features were selected using the technique named as feed-forward feature selection, from the set all 18 features.

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Multi-Lingual Scene Text Detection Using One-Class Classifier

Cited by 19 publications

References 31 publications

Building Machine Learning Models for Classification of Text and Non-text Elements in Natural Scene Images

Building Machine Learning Models for Classification of Text and Non-text Elements in Natural Scene Images

An Improved Scene Text Detection approach by utilising a Dual Discriminator framework

Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features

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