Multi-lingual Text Localization from Camera Captured Images Based on Foreground Homogenity Analysis

Dutta, I.; Chakraborty, Neelotpal; Mollah, Ayatullah Faruk; Basu, Subhadip; Sarkar, Ram

doi:10.1007/978-981-13-1280-9_15

Cited by 17 publications

(3 citation statements)

References 14 publications

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“…This method is particularly susceptible to blurring and parameter change, capturing only text components. Dutta et al [39] provide an alternate method to address these concerns, in which foreground homogeneity is accomplished by binning or grouping grey levels to segregate text components according to their stability and pixel density.Additional attempts have been made by Chakraborty et al [40] and Panda et al [41] to investigate the variation of the MSER parameters with reference to image dimension and text region dimension, thereby building a connection that dynamically changes MSER. The SWT concept was established by Ephstein et al [42] using Canny edge detection and subsequent transform on the edge image, which outputs the most probable distance and breadth of stroke for each pixel.…”

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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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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“…The visual images provide accurate and appropriate details for oblivious direction-finding, image perceptive, and retrieval methods, respectively [13]. This complex image frequently incorporates a variety of fonts and other properties [2].…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Based Approach for Extracting Textual Images from Deteriorate Images

Pandey¹,

Pandey²,

Wariya³

et al. 2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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INTRODUCTION:The quantity of audio and visual data is increasing exponentially due to the internet's rapid growth. The digital information in images and videos could be used for fully automated captions, indexing, and image structuring. The online image and video data system has seen a significant increase. In such a dataset, images and videos must be retrieved, explored, as well as inspected. OBJECTIVES: Text extraction is crucial for locating critical as well as important data. Disturbance is indeed a critical factor that affects image quality, and this is primarily generated during image acquisition and communication operations. An image can be contaminated by a variety of noise-type disturbances. A text in the complex image includes a variety of information which is used to recognise textual as well as non-textual particulars. The particulars in the complicated corrupted images have been considered important for individuals seeing the entire issue. However, text in complicated degraded images exhibits a rapidly changing form in an unconstrained circumstance, making textual data recognition complicated METHODS: The naïve bayes algorithm is a weighted reading technique is used to generate the correct text data from the complicated image regions. Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step. To restore the image's quality, the input image is processed employing gradient and contrast image methods. Following that, the contrast of the source images would be enhanced using an adaptive image map. Stroke width transform, Gabor transform, and weighted naïve bayes classifier methodologies have been used in complicated degraded images to segment, features extraction, and detect textual and non-textual elements. RESULTS: Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimization is being used. The dataset IIIT5K is used for the development portion, and also the performance of the suggested methodology is assessed by utilizing parameters like as accuracy, recall, precision, and F1 score. It performs well enough for record collections such as articles, even when significantly distorted, and is thus suitable for creating library information system databases CONCLUSION: A combination of deep neural network and particle swarm optimization is being used to recognise classified text. The dataset IIIT5K is used for the development portion, and while high performance is achieved with parameters such as accuracy, recall, precision, and F1 score, characters may occasionally deviate. Alternatively, the same character is frequently extracted [3] multiple times, which may result in incorrect textual data being extracted from natural images. As a result, an efficient technique for avoiding such flaws in the text retrieval process must be implemented in the near future.

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“…The visual images provide accurate and appropriate details for oblivious direction-finding, image perceptive, and retrieval methods, respectively [9]. This complex image frequently incorporates a variety of fonts and other properties [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Particle Swarm Optimisation-Based Techniques for Visually Impaired Humans' Text Recognition and Identification

Pandey

Wariya

et al. 2021

Augment Hum Res

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Multi-lingual Text Localization from Camera Captured Images Based on Foreground Homogenity Analysis

Cited by 17 publications

References 14 publications

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

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

A Deep Neural Network-Based Approach for Extracting Textual Images from Deteriorate Images

Deep Learning and Particle Swarm Optimisation-Based Techniques for Visually Impaired Humans' Text Recognition and Identification

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