Easyocr vs Tesseract (OCR Features Comparison)

Optical Character Recognition (OCR) is the technology that helps process documents, such as scanned paper documents, PDF files, or camera-captured high-resolution images, into printable and searchable data. The recognition of extracted text features and morphological operations allows OCR to automate data entry, which speeds up the information processing process and makes it more accurate.

OCR scans the document, recognizes the characters, such as letters, numbers, or symbols, and translates it into a machine-readable format. Its uses include book digitization, form processing, automation of document workflow, and improvement in accessibility to blind people. With the development of deep learning and AI, OCR engines became very accurate in recognizing complex formats, multi-language documents, and even poor-quality images.

Popular OCR tools and libraries, like EasyOCR, Tesseract OCR, Keras-OCR, and IronOCR, are commonly employed to integrate this functionality into modern applications.

EasyOCR

EasyOCR is an open-source Python library that aims to make text extraction from images simple and efficient. It uses deep learning techniques and supports over 80 languages, including Latin, Chinese, Arabic, and many others. Its API is simple enough that anyone can easily integrate OCR prediction functionality into their applications without much set-up. With EasyOCR Tesseract, one can do simple document digitization, license plate recognition, or even extract text from a picture.

EasyOCR is well known for its robust text recognition capabilities, especially with multi-line text and low-quality images. Therefore, it's suitable for real-world use cases, relying only on a few dependencies. It is lightweight and runs efficiently without the need for a GPU on modern hardware, making it quite attractive for developers in need of flexible OCR capabilities.

Features of EasyOCR

There are several features that make EasyOCR a comprehensive and powerful OCR utility:

• Recognizes over 80 languages: EasyOCR can read Chinese, Japanese, Korean, Arabic, Latin-based languages, and many more including complex words and languages.

• Advanced deep learning-based recognition: It supports advanced deep learning techniques with high performance and precision, especially in noisy or distorted text layouts and images.

• Simple API: This easy-to-use API lets users quickly get OCR capabilities within an application without further configuration.

• Multi-line text detection: It recognizes multiple lines of text, which is useful for documents, books, or multi-line signs.

• Lightweight: It runs well on the CPU and can leverage a GPU for improved performance, yet remains workable with basic hardware.

• Image pre-processing: Basic image pre-processing tools are available for cleaning up OCR output from noisy or low-resolution images.

• Flexible deployment: Works on various platforms and is relatively simple to embed in Python applications.

Installation

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.

Tesseract

Tesseract is one of the most popular open-source optical character recognition engines, supporting multiple hyperparameter options for customization. It can be accessed from Python applications using pytesseract. The development of Tesseract was initiated by Hewlett-Packard and later enhanced by Google. It is highly versatile, capable of extracting text from images and PDFs in more than 100 languages. The Python wrapper allows seamless interaction with Tesseract through pytesseract.

Tesseract is renowned for its ability to detect and extract machine-printed text. It offers multi-language recognition capabilities, supports training on new fonts, and performs text layout analysis. Tesseract is extensively used in digitizing documents, scanning receipts, automating data entry, and enabling searchable PDFs. In Python, Tesseract forms a powerful combination for developers working on OCR-related tasks.

Features of Tesseract OCR

Notable features of pytesseract include:

• Multi-language support: Tesseract can read over 100 languages, and pytesseract provides easy multi-language OCR support within Python scripts. It also allows training for additional custom fonts and languages, extending its capabilities.

• Image-to-text conversion: Pytesseract extracts text content from various image formats like PNG, JPEG, BMP, GIF, and TIFF, enabling OCR on diverse sources.

• Transformation from PDF to searchable PDF: Tesseract reads the text within a PDF file and converts it into a searchable format, allowing users to index the content of scanned documents.

• Complex text layout recognition: It can read complex layouts, including multi-column documents and tables, extracting text from non-standard formats more accurately.

• Custom configuration: Users can pass custom Tesseract configuration parameters through pytesseract to fine-tune OCR performance, using appropriate recognition modes or image attributes.

• Simple API: The simple API in pytesseract makes it easy for developers to add OCR to Python projects with minimal code for interaction.

This library works well with other libraries, such as OpenCV, PIL (Python Imaging Library), or NumPy, for image preprocessing to improve OCR accuracy.

Installation

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.

IronOCR

IronOCR is a powerful Optical Character Recognition library that allows .NET developers to leverage IronOCR for efficient text extraction from images, PDFs, and other document formats. Advanced algorithms provide high accuracy even for complex layouts or multi-language environments, supporting JPEG, PNG, GIF, and TIFF formats. The library offers configurable settings, enabling fine-tuning of the OCR engine process with parameters such as image resolution or text orientation.

The feature of image preprocessing ensures better quality input images result in higher recognition accuracy and further output documents as searchable PDF conversion for easier information retrieval. With its seamless integration into web applications, IronOCR is a strong choice for developers looking to implement reliable text extraction and document digitization solutions across various fields.

Features of IronOCR

• 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

Comparative Assessment

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.

Conclusion

In the realm of significant OCR libraries, IronOCR is distinguished by its superior accuracy, ease of integration, pre-processing capabilities, and creation of searchable PDFs. It adeptly handles complex layouts and noisy images while preserving document structure, supporting multiple languages out of the box. These features make it preferable over open-source solutions like Tesseract and EasyOCR.

Encompassing seamless integration with both .NET and Python, IronOCR serves as a comprehensive package for developers seeking high-quality OCR in diverse projects. Given its commendable performance, scalability, and commercial support, IronOCR is well-suited for extensive small and large-scale document digitization initiatives, offering reliable and efficient text recognition.

To learn more about IronOCR and its functionalities, you may visit the documentation page. For further details about Iron Software products, refer to the library suite page.