Text Extraction and Detection from Images using Machine Learning Techniques: A Research Review

Surana, Shivani; Pathak, Komal; Gagnani, Mehul; Shrivastava, Vidhan; Mahesh, T R; G, Sindhu Madhuri

doi:10.1109/icears53579.2022.9752274

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…On EMR databases, various machine learning techniques are used to search for hidden patterns that aid in cancer diagnosis [20]. Deep learning neural networks are utilized to evaluate CT and MRI scans, and natural language processing (NLP) is used to interpret doctor's prescriptions [21][22][23][24]. Big data and machine learning can be used for diagnosis.…”

Section: Machine Learningmentioning

confidence: 99%

Early Detection of Cancer using Machine Learning (ML) Techniques

Mahesh

Kumar²

2023

International Journal of Information Technology, Research and A

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Early detection of cancer sickness leads to rapid treatment, reducing the risk of morbidity and mortality. The diagnosis of oral cancer continues to be a challenge for dental careers, particularly in the location, evaluation, and review of early-stage oral disease. Due to the lack of optimal analysis using conventional methods, oral cancer is identified and grouped using AI at an early stage. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. A combination of expert AI and highlight determination calculations produces improved results in the early detection and forecast of oral cancer. The main goal and commitment of this audit study is to summarize the use of AI technologies for accurate early prediction of oral malignant development.

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Section: Machine Learningmentioning

confidence: 99%

Early Detection of Cancer using Machine Learning (ML) Techniques

Mahesh

Kumar²

2023

International Journal of Information Technology, Research and A

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“…The performance metrics used for evaluating the proposed method are MAE, MSE, MAPE, and R 2 . The obtained results are compared with existing methods [32][33][34] such as LSTM+RF+SVR [24], XGB-LGBM-MLP [26], TCN-GRU [28], CNN+LSTM [28], Hybrid GA-CNN [29], VMD-VAE-LSTM [30], and edRVFL [30].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Highway Self-Attention Dilated Casual Convolutional Neural Network Based Short Term Load Forecasting in Micro Grid

H S,

Ramaiah

2023

JMC

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Forecasting the electricity load is crucial for power system planning and energy management. Since the season of the year, weather, weekdays, and holidays are the key aspects that have an effect on the load consumption, it is difficult to anticipate the future demands. Therefore, we proposed a weather-based short-term load forecasting framework in this paper. First, the missing data is filled, and data normalisation is performed in the pre-processing step. Normalization accelerates convergence and improves network training efficiency by preventing gradient explosion during the training phase. Then the weather, PV, and load features are extracted and fed into the proposed Highway Self-Attention Dilated Casual Convolutional Neural Network (HSAD-CNN) forecasting model. The dilated casual convolutions increase the receptive field without significantly raising computing costs. The multi-head self-attention mechanism (MHSA) gives importance to the most significant time steps for a more accurate forecast. The highway skip network (HS-Net) uses shortcut paths and skip connections to improve the information flow. This speed up the network convergence and prevents feature reuse, vanishing gradients, and negative learning problems. The performance of the HSAD-CNN forecasting technique is evaluated and compared to existing techniques under different day types and seasonal changes. The outcomes indicate that the HSAD-CNN forecasting model has low Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and a high R2.

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“…1 shows an overview of text detection in images in the literature. In the pre-processing phase of image text detection, particularly in practical real-world applications, substantial efforts have been poured into the pursuit of swift and resilient algorithms [18][19][20][21]. The most important, among other challenges, is the imperative for outstanding performance, which includes achieving an acceptable processing time and a high recall rate while maintaining a low false alarm rate.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Image Text Detection via Pixel Standard Deviation Representation

Guia,

Laouid,

Hammoudeh

et al. 2024

CSSE

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Artificial intelligence has been involved in several domains. Despite the advantages of using artificial intelligence techniques, some crucial limitations prevent them from being implemented in specific domains and locations. The accuracy, poor quality of gathered data, and processing time are considered major concerns in implementing machine learning techniques, certainly in low-end smart devices. This paper aims to introduce a novel pretreatment technique dedicated to image text detection that uses the images' pixel divergence and similarity to reduce the image size. Mitigating the image size while keeping its features improves the model training time with an acceptable accuracy rate. The mitigation is reached by gathering similar image pixels in one pixel based on calculated values of the standard deviation σ , where we consider that two pixels are similar if they have approximately the same σ values. The work proposes a new pipeline approach that reduces the size of the image in the input and intermediate layers of a deep learning model based on merged pixels using standard deviation values instead of the whole image. The experimental results prove that this technique significantly improves the performance of existing text detection methods, particularly in challenging scenarios such as using low-end IoT devices that offer low contrast or noisy backgrounds. Compared with other techniques, the proposed technique can potentially be exploited for text detection in IoT-gathered multimedia data with reasonable accuracy in a short computation time. Evaluation of the MSRA-TD500 dataset demonstrates the remarkable performance of our approach, Standard Deviation Network (σ Net), with precision and recall values of 93.8% and 85.6%, respectively, that outperform recent research results.

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Text Extraction and Detection from Images using Machine Learning Techniques: A Research Review

Cited by 19 publications

References 11 publications

Early Detection of Cancer using Machine Learning (ML) Techniques

Early Detection of Cancer using Machine Learning (ML) Techniques

Highway Self-Attention Dilated Casual Convolutional Neural Network Based Short Term Load Forecasting in Micro Grid

Intelligent Image Text Detection via Pixel Standard Deviation Representation

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