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Text Extraction Project at IEEE Project Centers in Bangalore

Abstract:
Machine learning, a form of artificial intelligence, enables systems to learn and make predictions or judgments without explicit programming or human intervention. By utilizing existing knowledge and experience from a given training set, machine learning systems can perform specific tasks. In this project, we propose a text extraction approach that combines convolutional neural network (CNN) for image categorization and Tesseract, a recognition engine based on Long Short-Term Memory (LSTM), for text extraction from categorized images. LSTM networks, which are the building blocks of recurrent neural networks, are used to overcome the overfitting issue and improve the CNN's performance on large datasets. The system also focuses on multiple line text extraction rather than single line extraction, and increasing the dataset size and number of training epochs further enhance the system's accuracy. The architecture of the CNN, including the number of convolution and pooling layers and the number of nodes in each layer, is determined through a trial-and-error process. Additionally, CNNs require minimal preprocessing compared to other image classification techniques. At Smart AI Technologies, one of the top IEEE project centers in Bangalore, we offer complete support for your final year engineering project. Our components include:


Comprehensive documentation support to ensure a well-documented project.


Implementation of working hardware/software in your environment.

Customized classes and guidance tailored to your project requirements.
By choosing our project center in Bangalore, you can be confident that your final year engineering project on text extraction will be one of the best in the field of computer science. Contact us now to avail of our services and receive expert guidance throughout your project development process.

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