Im = np.ascontiguousarray(im]) # RGB To BGR Im = np.frombuffer(pix.samples, dtype=np.uint8).reshape( # pix.n = number of components per pixel (depends on the colorspace and alpha) # pix.samples = sequence of bytes of the image pixels like RGBA def pix2np(pix):Ĭonverts a pixmap buffer into a numpy array Obviously, you need to change it for your case. TESSERACT_PATH is where the Tesseract executable is located. TESSERACT_PATH = "C:\Program Files\Tesseract-OCR\tesseract.exe" Let's start by importing the necessary libraries: import os To get started, let's install the requirements: $ pip install Filetype=1.0.7 numpy=1.19.4 opencv-python=4.4.0.46 pandas=1.1.4 Pillow=8.0.1 PyMuPDF=1.18.9 pytesseract=0.3.7 This tutorial aims to develop a lightweight command-line-based utility to extract, redact or highlight a text included within an image or a scanned PDF file, or within a folder containing a collection of PDF files.
Pandas: is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.įiletype: Small and dependency-free Python package to deduce file type and MIME type. It is an essential module for image processing in Python. Pillow: is built on top of PIL (Python Image Library). Besides, Numpy can also be used as an efficient multi-dimensional container of generic data. It is the fundamental package for scientific computing with Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. Numpy: is a general-purpose array-processing package. PyMuPDF : MuPDF is a highly versatile, customizable PDF, XPS, and eBook interpreter solution that can be used across a wide range of applications as a PDF renderer, viewer, or toolkit. It can process images and videos to identify objects, faces, or even the handwriting of a human. OpenCV supports a wide variety of programming languages like Python, C++, Java, etc. OpenCV: is a Python open-source library, for computer vision, machine learning, and image processing. It is also useful as a stand-alone invocation script to tesseract, as it can read all image types supported by the Pillow and Leptonica imaging libraries, including jpeg, png, gif, bmp, tiff, and others. Python-tesseract: is a Python wrapper for Google’s Tesseract-OCR Engine. To validate Tesseract setup, please run the following command and check the generated output: However, you need to follow the official installation guide of Tesseract to install it on your operating system. Installing the Tesseract engine is outside the scope of this article. The best part is that it supports an extensive variety of languages. You can use it directly or can use the API to extract the printed text from images. In the year 2006, Tesseract was considered one of the most accurate open-source OCR engines. Tesseract OCR: is an open-source text recognition engine that is available under the Apache 2.0 license and its development has been sponsored by Google since 2006. To get started, we need to use the following libraries: Please note that this tutorial is about extracting text from images within PDF documents, if you want to extract all text from PDFs, check this tutorial instead. How to run an OCR scanner on a PDF file or a collection of PDF files.How to redact or highlight a specific text in an image file.How to run an OCR scanner on an image file.The following steps which may differ from one engine to another are roughly needed to approach automatic character recognition: Within this tutorial, I am going to show you the following: Generally, an OCR engine involves multiple steps required to train a machine learning algorithm for efficient problem-solving with the help of optical character recognition. OCR systems transform a two-dimensional image of text that could contain machine-printed or handwritten text from its image representation into machine-readable text. Optical character recognition (OCR) algorithms allow computers to analyze printed or handwritten documents automatically and prepare text data into editable formats for computers to efficiently process them.
Among them are invoices, receipts, corporate documents, reports, and media releases.įor those companies, the use of an OCR scanner can save a considerable amount of time while improving efficiency as well as accuracy. Nowadays, companies of mid and large scale have massive amounts of printed documents in daily use. Disclosure: This post may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission.
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