Why Recognize Text? If the document does not have any searchable text, then it significantly limits its functinality. The searchable text is added behind the page image, so the visual appearance of the Processes each page and creates an invisible layer of text that can be searched or copied and The "Recognize Text" operation (also known as "Optical Character Recognition" or OCR) Originally, the scanned PDF documents do not contain any searchable text.Įach page is just an image. This is the image that we will extract the text from.Recognize Text in Scanned PDF Documents Adobe® Acrobat® DC Tutorial Introduction This tutorial shows how to make scanned PDF documents searchable using "Recognize Text" operationĪvailable in the Adobe® Acrobat® software. Ensure that you have installed the required dependencies, including pytesseract, pillow, and the Tesseract OCR engine before running the code. The code opens an image file, performs OCR on it using pytesseract, and prints the extracted text. Line 11 (printing the extracted text): print(text) outputs the extracted text to the console. The image_to_string() function takes the image as an argument. It extracts the text from the image and assigns it to the variable text. Line 8 (performing OCR): text = pytesseract.image_to_string(image) uses the image_to_string() function from pytesseract to perform OCR on the image. You can provide images of any format, png or jpeg. Ensure the image file is in the same directory as your Python script, or provide the full path to the image file. Line 5 (opening the image file): image = Image.open('image.png') opens the image file named "image.png" using the Image.open() function from the PIL library. The from PIL import Image imports the Image class from the PIL (Python Imaging Library) package, used for image manipulation and processing. Line 1–2 (importing libraries): import pytesseract imports the pytesseract library, a Python wrapper for the Tesseract OCR engine. The provided code demonstrates how to extract text from an image using the pytesseract library in Python. You can install it using the following command: Pytesseract : The pytesseract library is used for OCR. The dependencies required include the following. This can speed up procedures, automate data entry, and make it possible to analyze documents effectively.Īccessibility: OCR converts printed or handwritten text into machine-readable formats that screen readers may read aloud or convert into braille, making text information accessible to those with visual impairments.ĭata extraction and analysis: OCR can extract text from pictures like bills, receipts, or forms, making it possible to extract data for additional analysis, automated data entry, or system integration.Ĭontent searchability: OCR technology effectively searches, indexes, and retrieves information from picture collections or scanned documents by converting image-based text into searchable and indexable digital text. Processing and automation of documents: OCR uses pertinent data extracted from images to process documents automatically. This lessens the need for human data entry and physical document management by enabling adequate information storage, retrieval, and sharing. Here are some significant arguments in favor of OCR.ĭigitization and archiving: OCR makes it possible to convert written documents from physical forms to digital ones. OCR technology’s ability to extract text from photos makes it essential in many industries. This makes it possible to perform tasks like digitizing printed documents, extracting information from photographs, and enabling text search inside image-based material. You can automate the extraction of text from photos using OCR techniques in Python. Python allows you to construct OCR algorithms, which examine the image, identify individual characters, and then extract the text that each character represents. OCR is a method for transforming scanned or photographed text pictures into text that is machine readable. Python requires optical character recognition (OCR) technology to extract image text.
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