LWSINet: A deep learning-based approach towards video script identification

Ghosh, Mridul; Mukherjee, Himadri; Obaidullah, Sk Md; Santosh, K. C.; Das, Nibaran

doi:10.1007/s11042-021-11103-8

Cited by 15 publications

(4 citation statements)

References 45 publications

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“…To evaluate the performance of our LWSNet, we used different evaluation metrics since accuracy is not sufficient [ 13 ] to justify the performance of the proposed framework, especially in disease detection cases. The classification of diseases is based directly on the number of True Positives, False Negatives, True Negatives, and False Positives.…”

Section: Methodsmentioning

confidence: 99%

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery

Lasker

Ghosh²,

Obaidullah

et al. 2022

Multimed Tools Appl

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Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.

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Section: Methodsmentioning

confidence: 99%

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery

Lasker

Ghosh²,

Obaidullah

et al. 2022

Multimed Tools Appl

Self Cite

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“…The algorithm is able to classify the geometrical forms constituting the patterns, even if they are partially deformed. This deep learning [54] is a type of machine learning that eliminates the need for manual processing of features. Images are immediately fed into this system, and the final categorization is returned.…”

Section: Proposed Methodsmentioning

confidence: 99%

Classification of Geometric Forms in Mosaics Using Deep Neural Network

et al. 2021

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The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.

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“…Around 2322 videos in these languages recorded with controlled camera movements like tilt, pan, etc., are additionally shared. Ghosh et al propose a LWSINet [38] for video data and a shallow convolutional neural network (SCNN)-based architecture [39] for image data for script identification allows improved functionality over low-resource scripts, especially Indic scripts. Huang et al [40] propose a scalable end-to-end trainable Multilingual Mask TextSpotter, which optimizes script identification while maintaing multiple recognition heads for different scripts.…”

Section: Work Of Photo-ocr On Latin Datasetsmentioning

confidence: 99%

Improving Scene Text Recognition for Indian Languages with Transfer Learning and Font Diversity

2022

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Reading Indian scene texts is complex due to the use of regional vocabulary, multiple fonts/scripts, and text size. This work investigates the significant differences in Indian and Latin Scene Text Recognition (STR) systems. Recent STR works rely on synthetic generators that involve diverse fonts to ensure robust reading solutions. We present utilizing additional non-Unicode fonts with generally employed Unicode fonts to cover font diversity in such synthesizers for Indian languages. We also perform experiments on transfer learning among six different Indian languages. Our transfer learning experiments on synthetic images with common backgrounds provide an exciting insight that Indian scripts can benefit from each other than from the extensive English datasets. Our evaluations for the real settings help us achieve significant improvements over previous methods on four Indian languages from standard datasets like IIIT-ILST, MLT-17, and the new dataset (we release) containing 440 scene images with 500 Gujarati and 2535 Tamil words. Further enriching the synthetic dataset with non-Unicode fonts and multiple augmentations helps us achieve a remarkable Word Recognition Rate gain of over 33% on the IIIT-ILST Hindi dataset. We also present the results of lexicon-based transcription approaches for all six languages.

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LWSINet: A deep learning-based approach towards video script identification

Cited by 15 publications

References 45 publications

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery

Classification of Geometric Forms in Mosaics Using Deep Neural Network

Improving Scene Text Recognition for Indian Languages with Transfer Learning and Font Diversity

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