Enhanced Feature Model Based Hybrid Neural Network for Text Detection on Signboard, Billboard and News Tickers

Anbukkarasi, S; Sathishkumar, Veerappampalayam Easwaramoorthy; Dhivyaa, C R; Cho, Jaehyuk

doi:10.1109/access.2023.3264569

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…To verify the effectiveness of the proposed method in this article, experiments were first conducted on public datasets. SynthText 27) was chosen as the training set, and IIIT5K-Words (IIIT5K) 28) , Street View Text (SVT) 29) , and ICDAR2013 (IC13) 30) were selected as the testing sets.…”

Section: Datasets and Metrics (1) Public Datasetsmentioning

confidence: 99%

Recognition of Hot-Cast Billet Numbers Based on ICRNN

Yi,

Liu,

Huang

et al. 2024

ISIJ Int.

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In steel production, the recognition of hot-cast billet numbers suffers from low efficiency and susceptibility to misjudgment. This paper proposes a novel method for identifying hot-cast billet numbers on the basis of improved Convolutional Recurrent Neural Network (ICRNN). Although the existing CRNN has achieved acceptable results in text recognition and music symbol recognition, it is not effective in recognizing industrial characters that are blurred or low-contrast. Based on the theoretical framework of CRNN, the Grayscale Spatial Transformation Network (GSTN) is added before character recognition to rectify the skew caused by shooting angles. The backbone network for feature extraction is changed to ResNet50. Moreover, the Efficient Channel Attention (ECA) module is added to construct the Res-ECA network, which extracts more features of the billet number characters. In the sequence modeling stage, the Bidirectional Gated Recurrent Unit (BiGRU) is used to reduce the risk of overfitting and accelerate convergence. After experimental comparison on a self-made billet number dataset, the put forward ICRNN has faster recognition speed and higher accuracy, with a recognition accuracy of 99.49%, which is 4.8% higher than that of the CRNN. The result fully demonstrates that ICRNN meets the requirements of accuracy and speed for billet number recognition.

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Section: Datasets and Metrics (1) Public Datasetsmentioning

confidence: 99%

Recognition of Hot-Cast Billet Numbers Based on ICRNN

Yi,

Liu,

Huang

et al. 2024

ISIJ Int.

View full text Add to dashboard Cite

show abstract

“…Sentiment analysis and offensive language identification for low resource code-mixed data in Tamil and English had been implemented using machine learning, deep learning and pre-trained models like BERT, RoBERTa and adapter-BERT (4) . Transliteration of English to Tamil Unicode characters had been implemented using phonetics based forward list processing (5) by Anbukkarasi, S. et al A combination of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) had been used to recognize the text in natural images (6) .…”

Section: Introductionmentioning

confidence: 99%

An Empirical Analysis of Language Detection in Dravidian Languages

Shimi,

Mahibha,

Thenmozhi

2024

IJST

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Objectives: Language detection is the process of identifying a language associated with a text. The proposed system aims to detect the Dravidian language that is associated with the given text using different machine learning and deep learning algorithms. The paper presents an empirical analysis of the results obtained using the different models. It also aims to evaluate the performance of a language agnostic model for the purpose of language detection. Method: An empirical analysis of Dravidian language identification in social media text using machine learning and deep learning approaches with k-fold cross validation has been implemented. The identification of Dravidian languages, including Tamil, Malayalam, Tamil Code Mix, and Malayalam Code Mix, is performed using both machine learning (ML) and deep learning algorithms. The machine learning algorithms used for language detection are Naive Bayes (NB), Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). The supervised Deep Learning (DL) models used include BERT, mBERT and language agnostic models. Findings: The language agnostic model outperform all other models considering the task of language detection in Dravidian languages. The results of both the ML and DL models are analyzed empirically with performance measures like accuracy, precision, recall, and f1-score. The accuracy associated with different machine learning algorithms varies from 85% to 89%. It is evident from the experimental result that the deep learning model outperformed with an accuracy of 98%. Novelty: The proposed system emphasizes on the use of the language agnostic model to implement the process of detecting Dravidian languages associated with the given text which provides a promising result of 98% accuracy which is higher than the existing methodologies. Keywords: Language, Machine learning, Deep learning, Transformer model, Encoder, Decoder

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