ABSTRACT:

Text detection and recognition in natural scene images present significant challenges due to varying orientations, distortions, complex backgrounds, and inconsistent lighting conditions. Traditional OCR techniques struggle in such environments as they are primarily designed for structured documents. This project addresses these challenges by integrating YOLO (You Only Look Once) for robust text detection and Tesseract OCR for text recognition. Annotations were created using the "Label Annotation" tool to train the YOLO model effectively. Additionally, advanced image preprocessing techniques, including grayscale conversion, Gaussian blur, and bilateral filtering, were applied to enhance text clarity and improve recognition accuracy. The detected text regions were extracted, and accuracy was evaluated to measure the system's performance. The proposed system significantly enhances text readability and extraction accuracy, making it well-suited for real-world applications such as document digitization, automated information retrieval, assistive technologies for visually impaired users, and real-time smart applications. By leveraging deep learning-based text detection and optimized OCR techniques, this work provides an efficient, scalable, and high-accuracy solution for natural scene text recognition.

INTRODUCTION:

Text detection and recognition in natural scene images is a critical task in computer vision, with numerous applications in areas such as document digitization, automated information retrieval, intelligent surveillance, assistive reading technologies, and real-time translation systems. Unlike traditional Optical Character Recognition (OCR) techniques that are optimized for structured documents with clear text layouts, text recognition in natural scenes presents significant challenges. The presence of varying fonts, arbitrary orientations, inconsistent lighting conditions, occlusions, distortions, and complex backgrounds makes it difficult to achieve high accuracy using conventional methods. Overcoming these challenges requires the integration of advanced text detection models, robust image preprocessing techniques, and efficient OCR algorithms.
This project focuses on developing a high-accuracy text detection and recognition system by leveraging YOLO (You Only Look Once) for text localization and Tesseract OCR for text extraction. YOLO is a deep learning-based object detection model known for its speed and accuracy in real-time applications. Unlike region-based approaches that perform multiple passes over an image, YOLO treats object detection as a single regression problem, predicting bounding boxes and class probabilities directly from the input image in one forward pass through the network. This makes it highly efficient for real-time text detection tasks.

OVERVIEW OF YOLO FOR TEXT DETECTION:

YOLO operates using a convolutional neural network (CNN) that divides an input image into a grid and predicts bounding boxes along with confidence scores for each region. The key advantage of YOLO over traditional text detection models is its ability to detect text in a single step, making it significantly faster than region-based methods like Faster R-CNN. Additionally, YOLO is capable of detecting text in arbitrary orientations, which is crucial for real-world scenarios where text may appear at various angles, curved patterns, or even partially occluded by objects.
To train YOLO for text detection, the "Label Annotation" tool was used to create custom annotations. These labeled datasets were then used to fine-tune the model, enabling it to recognize text effectively in natural scene images. The model learns to differentiate between text and non-text regions while accurately localizing text irrespective of its position, size, or orientation.
Once text regions are detected, they undergo a series of preprocessing steps to enhance clarity and improve recognition accuracy. Image preprocessing techniques such as grayscale conversion, Gaussian blur, and bilateral filtering are applied to reduce noise, enhance contrast, and preserve important text features. These preprocessing methods help refine the text regions before passing them to the OCR engine, ensuring improved readability and extraction accuracy.

TESSERACT OCR FOR TEXT RECOGNITION:

After text detection, the extracted text regions are processed using Tesseract OCR, an open-source optical character recognition engine widely used for text extraction. Tesseract utilizes deep learning-based LSTM (Long Short-Term Memory) networks to recognize and convert detected text into machine-readable format. However, Tesseract’s performance can be significantly affected by variations in image quality, making the preprocessing step crucial for enhancing OCR accuracy. By applying the optimized image enhancement techniques, the system ensures better text recognition even in challenging environments with poor lighting, distortions, or background clutter.