googlefc.controlledMessagingFunction } googlefc.controlledMessagingFunction

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atOptions = { 'key' : '38f60b3ea4c014182af88bbdf11d235c', 'format' : 'iframe', 'height' : 60, 'width' : 468, 'params' : {} }; document.write(''); Preprocessing: The acquired image often undergoes preprocessing to enhance its quality and make it more suitable for OCR. This may include tasks like noise reduction, image rotation, resizing, and contrast adjustment. Text Detection: OCR software identifies regions within the image where text is located. This step involves detecting lines, words, and individual characters in the image. Modern OCR systems often use advanced computer vision techniques for accurate text detection. Character Segmentation: Once text regions are identified, the OCR system segments the image into individual characters or words. This step is crucial for recognizing and interpreting each element correctly. Feature Extraction: The system extracts features from the segmented characters or words. Features could include information about the shape, size, and orientation of each character. Character Recognition: Using the extracted features, the OCR system compares the characters against a predefined set of patterns or a trained model. This comparison helps recognize and identify each character. Post-processing: After character recognition, post-processing steps may be applied to refine the results. This could involve correcting errors, handling ambiguous characters, and improving overall accuracy. Text Output: Finally, the recognized text is outputted in a readable format. The output can be in various forms, such as plain text, a document file, or any other desired format. OCR technology has advanced significantly with the advent of deep learning and neural networks. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have greatly improved the accuracy and efficiency of OCR systems, enabling them to handle diverse fonts, languages, and document layouts. Popular OCR tools and libraries include Tesseract, OCRopus, and Google Cloud Vision API. These tools are often used in various applications, such as document digitization, text extraction from images, and automation of data entry tasks.