Easyocr 与 Tesseract（OCR 功能比较）

EasyOCR can be installed using pip, Python's package manager. Ensure that all the dependencies have been satisfied first. The essential dependencies include PyTorch libraries: torch and torchvision. These can be installed together with EasyOCR:

Install EasyOCR: Open a terminal or command line and enter the command:

pip install easyocr

pip install easyocr

Install PyTorch, if not installed (required by EasyOCR): EasyOCR runs on PyTorch. If not automatically installed in your environment, follow the official PyTorch installation guide.

Once installed, you'll be ready to use EasyOCR for text extraction tasks.

OCR Image using EasyOCR

The following is a sample Python code demonstrating how to use EasyOCR to perform OCR on an image:

import easyocr
import matplotlib.pyplot as plt
import cv2

# Initialize the EasyOCR reader with the English language specified
reader = easyocr.Reader(['en'])  # Specify the languages (e.g., 'en' for English)

# Load the image
image_path = 'sample_image.png'  # Path to the image
image = cv2.imread(image_path)

# Perform OCR on the image
result = reader.readtext(image_path)

# Print detected text and its bounding boxes
for (bbox, text, prob) in result:
    print(f"Detected Text: {text} (Confidence: {prob:.4f})")

# Optionally, display the image with bounding boxes around the detected text
for (bbox, text, prob) in result:
    # Unpack the bounding box
    top_left, top_right, bottom_right, bottom_left = bbox
    top_left = tuple(map(int, top_left))
    bottom_right = tuple(map(int, bottom_right))

    # Draw a rectangle around the text
    cv2.rectangle(image, top_left, bottom_right, (0, 255, 0), 2)

# Convert the image to RGB (since OpenCV loads images in BGR by default)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Display the image with bounding boxes
plt.imshow(image_rgb)
plt.axis('off')
plt.show()

import easyocr
import matplotlib.pyplot as plt
import cv2

# Initialize the EasyOCR reader with the English language specified
reader = easyocr.Reader(['en'])  # Specify the languages (e.g., 'en' for English)

# Load the image
image_path = 'sample_image.png'  # Path to the image
image = cv2.imread(image_path)

# Perform OCR on the image
result = reader.readtext(image_path)

# Print detected text and its bounding boxes
for (bbox, text, prob) in result:
    print(f"Detected Text: {text} (Confidence: {prob:.4f})")

# Optionally, display the image with bounding boxes around the detected text
for (bbox, text, prob) in result:
    # Unpack the bounding box
    top_left, top_right, bottom_right, bottom_left = bbox
    top_left = tuple(map(int, top_left))
    bottom_right = tuple(map(int, bottom_right))

    # Draw a rectangle around the text
    cv2.rectangle(image, top_left, bottom_right, (0, 255, 0), 2)

# Convert the image to RGB (since OpenCV loads images in BGR by default)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Display the image with bounding boxes
plt.imshow(image_rgb)
plt.axis('off')
plt.show()

The below image is the output generated from the above code.

After installing Tesseract, install the pytesseract package using pip:

pip install pytesseract

pip install pytesseract

OCR image using pytesseract

Here's a sample Python code using pytesseract to perform OCR on an image:

import pytesseract
from PIL import Image

# Set the path to the Tesseract executable
pytesseract.pytesseract.tesseract_cmd = r'<full_path_to_your_tesseract_executable>'

# Open the image and perform OCR
image = Image.open('sample_image.png')
text = pytesseract.image_to_string(image)

# Print the extracted text
print(text)

import pytesseract
from PIL import Image

# Set the path to the Tesseract executable
pytesseract.pytesseract.tesseract_cmd = r'<full_path_to_your_tesseract_executable>'

# Open the image and perform OCR
image = Image.open('sample_image.png')
text = pytesseract.image_to_string(image)

# Print the extracted text
print(text)

Below is the output generated from the above code.

• High Accuracy: Uses advanced algorithms to provide high accuracy levels in text recognition regardless of document complexity or font usage.

• Multiple Formats Support: Accepts image formats like JPEG, PNG, GIF, and TIFF, in addition to PDFs, for versatility across applications.

• Multilingual Recognition: Supports multilingual OCR, yielding accurate results in diverse linguistic contexts.

• Text Layout Preservation: Maintains the original document layout, ensuring extracted text retains its formatted structure.

• Configurable OCR: Offers configurable parameters for image resolution, text orientation, and more, allowing developers to optimize OCR performance for specific images.

• Image Preprocessing: Includes basic tools for enhancing images, such as noise removal, contrast adjustment, and resizing, to improve OCR accuracy.

• Searchable PDF Conversion: Converts scanned images and documents directly into searchable PDFs for efficient data management and retrieval.

• Easy Integration: Facilitates straightforward integration into .NET applications, allowing users to easily add OCR functionality.

• Batch Processing: Supports processing multiple images or documents simultaneously, useful for handling large volumes of data.

Installation

To install IronOCR, open NuGet Package Manager in Visual Studio, start a new project, search for "IronOCR," select the latest version, and click Install.

Sample Code using IronOCR

The following C# code demonstrates how to use IronOCR for OCR processing:

using IronOcr;

class Program
{
    static void Main(string[] args)
    {
        // Initialize IronTesseract engine
        var Ocr = new IronTesseract();

        // Add languages to the OCR engine
        Ocr.Language = OcrLanguage.English;

        // Define the path to the input image
        var inputFile = @"path\to\your\image.png";

        // Read the image and perform OCR
        using (var input = new OcrInput(inputFile))
        {
            var result = Ocr.Read(input);

            // Display the extracted text
            Console.WriteLine("Text:");
            Console.WriteLine(result.Text);
        }
    }
}

using IronOcr;

class Program
{
    static void Main(string[] args)
    {
        // Initialize IronTesseract engine
        var Ocr = new IronTesseract();

        // Add languages to the OCR engine
        Ocr.Language = OcrLanguage.English;

        // Define the path to the input image
        var inputFile = @"path\to\your\image.png";

        // Read the image and perform OCR
        using (var input = new OcrInput(inputFile))
        {
            var result = Ocr.Read(input);

            // Display the extracted text
            Console.WriteLine("Text:");
            Console.WriteLine(result.Text);
        }
    }
}

Imports IronOcr

Friend Class Program
	Shared Sub Main(ByVal args() As String)
		' Initialize IronTesseract engine
		Dim Ocr = New IronTesseract()

		' Add languages to the OCR engine
		Ocr.Language = OcrLanguage.English

		' Define the path to the input image
		Dim inputFile = "path\to\your\image.png"

		' Read the image and perform OCR
		Using input = New OcrInput(inputFile)
			Dim result = Ocr.Read(input)

			' Display the extracted text
			Console.WriteLine("Text:")
			Console.WriteLine(result.Text)
		End Using
	End Sub
End Class

High Accuracy

IronOCR stands out for its accuracy with complex layouts, noisy images, and low-resolution texts when compared to Tesseract or EasyOCR. Its built-in image preprocessing tools, such as noise reduction and contrast adjustments, contribute to achieving high accuracy in real-world applications.

Multi-format and Layout Preservation

IronOCR excels in processing various image formats, PDF files, and multi-column layouts while preserving original document structure and formatting. It is well-suited for projects where layout preservation is paramount.

Its capability to directly convert images and scanned documents into fully searchable PDFs without relying on additional tools or libraries gives it an advantage over Tesseract and EasyOCR.

IronOCR Provides Advanced Preprocessing

Even poor-quality images can achieve high OCR accuracy using IronOCR’s advanced preprocessing features, which reduce the need for additional libraries like OpenCV, making it a comprehensive solution for text extraction.

Scalability and Performance

Optimized for high-speed, resource-efficient OCR, IronOCR supports scalability for large document processing tasks, a priority for enterprise applications.

Support and Updates

With commercial support, IronOCR benefits from regular updates, bug fixes, and dedicated assistance, offering long-term reliability and the latest advancements in OCR, unlike open-source options such as Tesseract and EasyOCR.