Text Localization: EAST vs. CRAFT Detectors

In modern computer vision, text localization refers to the process of detecting the position of text within an image. This is a foundational step before Optical Character Recognition (OCR), which then extracts the actual text content. Two of the most powerful deep learning-based methods for text detection are EAST (Efficient and Accurate Scene Text Detector) and CRAFT (Character Region Awareness for Text detection).

This guide explores the concepts, architectures, differences, use-cases, and implementation tips for EAST and CRAFT detectors.

What is Text Localization?

Text localization involves identifying bounding boxes around text regions in images. It is critical for various applications:

• Scene Text Recognition: Detecting text in natural scenes (e.g., signs, street names, shop fronts).
• Document Processing: Locating text fields in scanned documents, forms, and invoices.
• Automated Form Filling: Identifying areas where specific information needs to be entered.
• Number Plate Recognition: Extracting license plates from vehicle images.

The primary goal is to precisely locate the position and shape of text before feeding it to an OCR engine like Tesseract or EasyOCR.

EAST Text Detector

Overview

EAST (Efficient and Accurate Scene Text Detector) is a real-time text detector capable of detecting text with arbitrary orientations. It achieves this using a single, fully convolutional neural network (FCN). Introduced by Google Research, EAST simplifies the pipeline by performing both text detection and geometry prediction within a single model.

Key Features

• Multi-Oriented Text Detection: Can detect text at various angles.
• Output Formats: Outputs rotated rectangles or quadrilaterals to better fit text.
• End-to-End Trainable: The entire network can be trained together.
• Speed and Accuracy: Designed for fast inference while maintaining high accuracy.

Architecture

The EAST architecture comprises several key components:

1. Backbone Network: Utilizes Convolutional Neural Networks (CNNs) such as PVANet or VGG16 to extract feature maps from the input image. These feature maps capture hierarchical visual information.
2. Feature Fusion: Integrates features from multiple scales of the backbone network. This allows the detector to effectively identify text instances of various sizes, from small characters to larger text blocks.
3. Output Layers: The network produces two primary output maps:
   • Score Map: A probability map indicating the likelihood of text presence at each pixel location.
   • Geometry Map: Predicts the bounding box coordinates for each text instance. This can include angle information to define rotated rectangles or quadrilaterals.

Advantages

• Real-time Performance: Highly efficient for applications requiring fast processing.
• High Accuracy: Performs well on a wide range of scene text images.
• Simplicity: A single model simplifies deployment and integration.

Usage Example (OpenCV Pre-trained Model)

import cv2
import numpy as np

# Load the pre-trained EAST model
net = cv2.dnn.readNet('frozen_east_text_detection.pb')

# Load the input image
image = cv2.imread('text_image.jpg')

# Prepare the input blob for the network
# Image dimensions are resized to 320x320, mean subtraction is applied
blob = cv2.dnn.blobFromImage(image, 1.0, (320, 320),
                             (123.68, 116.78, 103.94), True, False)

# Set the input to the network
net.setInput(blob)

# Perform forward pass to get scores and geometry maps
scores, geometry = net.forward(['feature_fusion/Conv_7/Sigmoid', 'feature_fusion/concat_3'])

# Post-processing is required to extract bounding boxes from the output maps
# (Details of post-processing are omitted for brevity but involve thresholding and NMS)

CRAFT Text Detector

Overview

CRAFT (Character Region Awareness for Text detection) operates at a finer granularity by detecting individual characters and then linking them together to form words. Unlike EAST, which aims to detect words or lines directly, CRAFT focuses on identifying character regions first, making it particularly effective for complex or irregular text.

Key Features

• Character-Level Detection: Identifies individual characters as the primary units.
• Irregular and Curved Text: Performs exceptionally well on text that is curved, distorted, or uses non-standard fonts.
• Artistic Text: Suitable for detecting stylized or artistic text where standard bounding boxes may not suffice.

Architecture

CRAFT's architecture is built around the following components:

1. Backbone Network: Typically uses VGG16 to extract features from the input image.
2. Character Region Map: This output map highlights the regions corresponding to individual characters.
3. Affinity Map: This map indicates the relationships or affinities between adjacent characters, signaling whether they belong to the same word.
4. Linking and Post-processing: After generating the character region and affinity maps, a post-processing step groups connected characters based on their affinity scores to form words and their corresponding bounding boxes.

Advantages

• Handles Curved and Artistic Text: Excels where EAST might struggle due to text deformities.
• Robust to Spacing Variations: Less sensitive to varying character spacing within words.
• High Localization Precision: Achieves very accurate bounding box predictions at the character and word level.

Example Usage (via PyTorch)

CRAFT is often used through its official PyTorch implementation.

# Clone the official repository
git clone https://github.com/clovaai/CRAFT-pytorch.git
cd CRAFT-pytorch

Sample Inference Script (Conceptual):

from craft import CRAFT
from utils import test_net # Assuming test_net is provided in utils

# Initialize the CRAFT model (loads pre-trained weights by default)
craft_model = CRAFT()

# Path to the image for inference
image_path = 'sample_image.jpg'

# Perform detection
# bboxes: bounding boxes for words
# polys: polygons for words (more precise than bounding boxes)
# score_text: the detected text
bboxes, polys, score_text = test_net(craft_model, image_path)

# Further processing would involve drawing these bboxes/polys on the image
# and potentially feeding the cropped text regions to an OCR engine.

Comparison: EAST vs. CRAFT

Use Cases of Text Localization

Text localization plays a vital role in various applications:

• Document Scanning and Analysis: Identifying text areas in structured forms, invoices, and contracts for automated data extraction.
• Street Scene Understanding: Detecting street signs, shop names, advertisements, and traffic information in natural environments.
• Autonomous Driving: Reading road signs, vehicle number plates, and other contextual text.
• Accessibility: Enabling visually impaired users to access text content from images.
• Multilingual Environments: Locating and recognizing text in signs and documents across different languages.

Tips for Better Performance

• Image Resolution: Use high-resolution images for better accuracy, especially for detecting small text.
• Model Selection:
  • For real-time applications or scenes with predominantly horizontal/slightly rotated text, EAST is often the preferred choice.
  • For complex layouts, curved text, artistic fonts, or when high localization precision is paramount, CRAFT is generally more suitable.
• Pipeline Integration: Combine text localization models (EAST or CRAFT) with OCR engines (like Tesseract, EasyOCR, or PaddleOCR) to build complete text recognition systems.
• Post-processing: Fine-tune post-processing steps (e.g., confidence thresholds, Non-Maximum Suppression) for optimal results based on your specific dataset.
• Data Augmentation: Employ data augmentation techniques during training to improve robustness to variations in scale, rotation, and appearance.

Conclusion

Both EAST and CRAFT are powerful and widely used deep learning models for text localization. EAST offers an excellent balance of speed and accuracy, making it ideal for real-time applications and standard text detection. CRAFT, with its character-aware approach, provides superior performance for challenging text scenarios, including curved, artistic, or highly stylized text. The choice between them depends on the specific requirements of your application, including the nature of the text to be detected and the computational constraints.

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Interview Questions

1. What is text localization, and why is it important in OCR pipelines?
2. Explain the architecture and workflow of the EAST text detector.
3. How does CRAFT differ from EAST in its detection strategy?
4. In what scenarios would you prefer CRAFT over EAST for text detection?
5. What are the primary outputs of the EAST detector, and how are bounding boxes extracted from them?
6. Describe the role of the Character Region Map and Affinity Map in the CRAFT architecture.
7. How does the character-level approach in CRAFT contribute to its effectiveness in detecting curved or artistic text?
8. Compare the accuracy and performance trade-offs between EAST and CRAFT for multilingual or irregular fonts.
9. How would you integrate an EAST or CRAFT model with OCR engines like Tesseract or EasyOCR?
10. What preprocessing steps or post-processing techniques can improve the accuracy of deep learning-based text localization models?