7 Steps of Image Pre-Processing to Improve Ocr Using Python
What is OCR??
Optical Character Recognition (OCR) is a course of perceiving text inside pictures and changing it into an electronic structure. These pictures could be of manually written text, printed text like records, receipts, name cards, and so forth, or even a characteristic scene photo. OCR uses two techniques together to extract text from any image. First, it must do text detection to determine where the text resides in the image. In the second technique, OCR recognizes and extracts the text using text recognition techniques. OCR is an active research area and with the introduction of deep learning, the performance of various OCR models has been increased sufficiently.?
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What are the application areas for OCR??
OCR has many application areas in the real world and one particularly important benefit is to minimize the human effort across various industries in our everyday life. Some of the popular application areas for OCR are the digitization of various paperwork, book scanning, reading signboards to translate into various languages, reading signboards for self-driving cars, registration number extraction from vehicle number plates, handwritten recognition tasks, etc.?
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Why does image pre-processing important for any OCR model’s performance??
We have consolidated seven useful steps for pre-processing the image before providing it to #OCR for text extraction. Explain these pre-processing steps, we are going to use OpenCV and Pillow library.?
Seven steps to perform image pre-processing for OCR?
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Conclusion:?
OCR has a wide range of application areas in the real world and improving the performance of OCR models is necessary to avoid mistakes in the real world. Image pre-processing reduces the error by a significant margin and helps to perform OCR better. #Imagepreprocessing steps can be decided based on the images available for text extraction. Based on the image, some steps can be removed, and some others can be added as per requirement. The pre-processing becomes more effective when applied after having a better understanding of the input data (images) and the task to perform.?