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How can I improve the accuracy of my program in detecting text boxes in images or documents?
Text detection and text recognition are not the same.
Text detection is the process of localizing where text is in an image, while text recognition identifies what is written in the text box.
Deep learning-based text detection has become popular due to its high accuracy and speed.
EAST (Efficient and Accurate Scene Text Detector) is a deep learning-based text detector that can run at near real-time on 720p images.
OpenCV has a built-in text detection module based on EAST that can be used to detect text in images.
Text localization can be thought of as a specialized form of object detection where the goal is to compute bounding boxes for every region of text in an image.
Tesseract OCR is an open-source Optical Character Recognition (OCR) engine that can be used for both text localization and text detection.
In text detection, the model only detects the bounding boxes around the text.
However, in text recognition, the model actually finds what is written in the box.
Deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are commonly used for text detection and recognition tasks.
When detecting text in natural scene images, the background can affect the accuracy of the text detection model.
Pre-processing techniques like image binarization, noise reduction, and thresholding can improve the accuracy of text detection models.
Post-processing techniques like Non-Maximum Suppression (NMS) can be used to remove duplicate bounding boxes and improve the accuracy of text detection models.
The accuracy of text detection models depends on various factors such as image quality, text size, font style, and lighting conditions.
Text detection models can be trained on specific datasets to improve their accuracy in detecting text in certain domains, such as medical images, license plates, or invoices.
Real-time text detection can be used in various applications such as augmented reality, robotics, and autonomous vehicles.
Transfer learning is a technique used to leverage pre-trained models for text detection tasks.
This technique can help reduce the training time and improve the accuracy of the text detection model.
The precision and recall metrics are commonly used to evaluate the performance of text detection models.
Text detection models can be integrated with OCR engines to extract text from images and documents.
Text detection models can be deployed on various platforms such as mobile devices, edge devices, and cloud servers.
The accuracy of text detection models can be improved by using techniques like data augmentation, transfer learning, and ensemble learning.
Text detection models can be used for various applications such as automated document processing, content-based image retrieval, and text-based advertising.
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