CHARACTER RECOGNITION IN NATURAL IMAGES USING NEURAL NETWORKS

Abstract

Character Recognition in Natural Images Using Neural Networks” addresses the challenge of identifying and interpreting characters embedded in natural scenes. Unlike scanned documents or printed text, characters in natural images are subject to variations in font, orientation, illumination, and background noise. This project leverages neural networks to extract and recognize characters in such challenging environments. By employing a combination of Convolutional Neural Networks (CNNs) for feature extraction and classification, the system achieves high accuracy in recognizing characters from natural images, making it suitable for applications such as autonomous navigation, augmented reality, and document digitization.

Introduction

Character recognition in natural images is a significant problem in computer vision, with applications in areas like automatic license plate detection, traffic sign reading, and text detection in real-world photographs. Unlike traditional optical character recognition (OCR), natural images often contain distortions, cluttered backgrounds, and varying lighting conditions, making the task more complex.

Advances in neural networks, particularly Convolutional Neural Networks (CNNs), have made it possible to achieve high accuracy in challenging image recognition tasks. This project aims to develop a robust character recognition system that identifies characters from natural images by combining deep learning techniques with pre-processing methods for feature enhancement.

Existing System

1. Traditional OCR Systems:
   • Rely on template matching and feature extraction.
   • Struggle with non-standard fonts, noise, and varying orientations in natural images.
2. Machine Learning-Based Systems:
   • Use handcrafted features combined with classifiers like Support Vector Machines (SVMs).
   • Limited adaptability to complex environments.
3. Deep Learning Models:
   • Some systems use CNNs, but these are often designed for specific datasets and may not generalize well to all types of natural images.

Proposed System

The proposed system introduces a deep learning-based character recognition framework optimized for natural images. Key features include:

• Pre-processing: Enhances image quality by reducing noise and normalizing illumination.
• Feature Extraction: Employs CNNs to automatically learn robust features from raw pixel data.
• Classification: Classifies detected characters using neural network-based classifiers.
• Post-processing: Refines recognition results using techniques like language modeling or dictionary-based correction.

Methodology

1. Data Collection and Preprocessing:
   • Use publicly available datasets like ICDAR, SVHN, or custom datasets containing characters in natural images.
   • Preprocess images to enhance contrast, remove noise, and segment characters.
2. Neural Network Design:
   • CNN Architecture:
     • Input layer for raw images.
     • Convolutional layers for feature extraction.
     • Pooling layers for dimensionality reduction.
     • Fully connected layers for classification.
   • Use transfer learning with pre-trained models like ResNet or VGG for better performance.
3. Training:
   • Train the model using labeled data with augmentation to simulate variations in lighting, orientation, and noise.
4. Evaluation:
   • Assess performance using metrics like accuracy, precision, recall, and F1-score.
   • Test on unseen images to evaluate generalization.
5. Deployment:
   • Integrate the model into a real-time application for detecting and recognizing characters in natural images.

Technologies Used

1. Programming Language: Python.
2. Frameworks: TensorFlow, PyTorch.
3. Libraries: OpenCV for image processing, NumPy for numerical computation.
4. Models: Custom CNNs or pre-trained architectures like ResNet, EfficientNet.
5. Datasets: ICDAR, Street View House Numbers (SVHN), or custom-curated datasets.
6. Hardware: NVIDIA GPUs for training and inference acceleration.