What is the PyTesseract Python Library and How do you Install it?

This recipe walks you through simple installation steps of PyTesseract - a user-friendly Python library for text extraction from images. | ProjectPro

Recipe Objective - What is the PyTesseract Python Library and How do you Install it? 

Pytesseract is a powerful optical character recognition (OCR) tool for Python, enabling the extraction of text from images. This Optical Character Recognition tool transcends conventional boundaries, supporting an array of image formats, including jpeg, png, and gif. Unlike conventional OCR methods, PyTesseract bypasses the need to save recognized text to files, offering a direct and efficient means of extracting textual information from images. Check out this recipe to uncover the complete installation process and basic usage of PyTesseract. 

Access Face Recognition Project Code using Facenet in Python

Links for the more related projects:-

/projects/data-science-projects/deep-learning-projects

/projects/data-science-projects/neural-network-projects

How to Install PyTesseract in Python? - A Step-by-Step Guide 

Follow the steps below to seamlessly integrate PyTesseract into your Python projects and witness its capabilities firsthand - 

  1. Installing Tesseract

To begin using pytesseract, you first need to install Tesseract. Follow these steps:

Visit the Tesseract GitHub page.

Download and run the Windows installer.

  1.  Note Tesseract Path

Note the tesseract path from the installation. At the time of this edit, the default installation path was: "C:\Users\USER\AppData\Local\Tesseract-OCR" It may change, so please check the installation path.

  1. Pip Install Pytesseract

Execute the following command in your terminal to install pytesseract using pip:

pip install pytesseract

  1. Set Tesseract Path in Script

Set the tesseract path in the script before calling "image_to_string":

pytesseract.pytesseract.tesseract_cmd = r'C:\Users\USER\AppData\Local\Tesseract-OCR\tesseract.exe'

Python Install Pytesseract - Simple Example 

Now that you have pytesseract installed and configured, here's a basic example of using it in a Python script - 

from PIL import Image

import pytesseract

# Set Tesseract path

pytesseract.pytesseract.tesseract_cmd = r'C:\Users\USER\AppData\Local\Tesseract-OCR\tesseract.exe'

# Open an image file

img = Image.open('your_image.png')

# Extract text from the image

text = pytesseract.image_to_string(img)

# Print the extracted text

print("Extracted Text:")

print(text)

Explore more Python Libraries with ProjectPro!  

PyTesseract proves to be a powerful tool for optical character recognition in Python, simplifying the extraction of text from images and enhancing various applications. By following the installation guide provided, users can seamlessly integrate PyTesseract into their projects and leverage its capabilities. As you delve into the realm of Python libraries, consider broadening your toolkit further with ProjectPro to explore and harness the full potential of cutting-edge libraries, empowering your data science journey with a diverse range of tools and functionalities. 

What Users are saying..

profile image

Ed Godalle

Director Data Analytics at EY / EY Tech
linkedin profile url

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More

Relevant Projects

Build a Graph Based Recommendation System in Python-Part 2
In this Graph Based Recommender System Project, you will build a recommender system project for eCommerce platforms and learn to use FAISS for efficient similarity search.

NLP and Deep Learning For Fake News Classification in Python
In this project you will use Python to implement various machine learning methods( RNN, LSTM, GRU) for fake news classification.

Time Series Forecasting Project-Building ARIMA Model in Python
Build a time series ARIMA model in Python to forecast the use of arrival rate density to support staffing decisions at call centres.

Ola Bike Rides Request Demand Forecast
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

Loan Eligibility Prediction in Python using H2O.ai
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.

Expedia Hotel Recommendations Data Science Project
In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.

MLOps Project to Deploy Resume Parser Model on Paperspace
In this MLOps project, you will learn how to deploy a Resume Parser Streamlit Application on Paperspace Private Cloud.

LLM Project to Build and Fine Tune a Large Language Model
In this LLM project for beginners, you will learn to build a knowledge-grounded chatbot using LLM's and learn how to fine tune it.

Build a Multi Class Image Classification Model Python using CNN
This project explains How to build a Sequential Model that can perform Multi Class Image Classification in Python using CNN

Multi-Class Text Classification with Deep Learning using BERT
In this deep learning project, you will implement one of the most popular state of the art Transformer models, BERT for Multi-Class Text Classification