Project Description:
In today’s world, where technology and artificial intelligence (AI) continue to advance, the need for accurate multi-script text detection from sign board images has become increasingly important. Imagine traveling in a multilingual country like India, where signboards are often written in more than two languages, both national and regional. Understanding these signs is vital for navigation, communication, and access to information.
The business objective of this project revolves around the critical role of multi-script text detection in the field of computer vision and AI. It aims to create a solution that can contribute to text translation systems, mobile-based OCR (Optical Character Recognition) systems, and various image-processing applications. The challenge lies in accurately detecting text from sign board images, especially when they contain multiple scripts, such as English, Hindi, and Gujarati.
Problem Statement:
Accurate multi-script text detection from sign board images is a challenging task due to the variability in languages, scripts, and fonts. Multilingual text detection adds an extra layer of complexity, as the multi-script environment can differ significantly. Furthermore, there was no labeled dataset available in the public domain, making it even more difficult to develop a robust solution.
Description of Solution:
To address this problem, a Faster RCNN-based method was proposed. Faster RCNN (Region-based Convolutional Neural Network) is a deep learning model that excels in object detection tasks. In this case, it was customized to detect English, Hindi, and Gujarati texts on sign board images.
Several parameters were meticulously designed to enable the detection of multi-script text from sign board images. Additionally, a dataset was created since there were no publicly available labeled datasets. This dataset was crucial for training and testing the model, ensuring its accuracy and effectiveness.
Tools and Technologies Used:
- Languages: Python
- Development Environment: Google Colab
Results:
The implementation of the Faster RCNN-based method yielded impressive results. The solution achieved efficient multilingual text detection from sign board images, surpassing the challenges posed by the variability in languages and scripts. Some notable outcomes include:
- Improved Recognition Accuracy: The model demonstrated a significant enhancement in the recognition accuracy of selected languages, namely English, Hindi, and Gujarati. This means that it can effectively identify and extract text from images, even when multiple scripts are present.
- Local Language Identification: The system also offers local language identification, which enhances the understanding of the content on sign boards. This feature is valuable for tourists, commuters, and anyone navigating in multilingual regions.
- Applicability: The solution is well-applicable to multi-script text detection in various real-world scenarios. It can be integrated into a wide range of image-processing applications, including language translation, information retrieval, image-to-text conversions, and assistance services.
Business Benefits:
The project’s outcomes have far-reaching business benefits:
- Primary Solution in Image Processing Applications: The multi-script text detection solution serves as a primary tool in many image-processing applications, addressing a critical need in industries such as transportation, tourism, and information services.
- Addressing Emerging Needs: In our everyday lives, the demand for accurate text detection from sign boards is growing. This solution addresses this emerging need, making it easier for people to access information and navigate unfamiliar environments.
- Improved Efficiency: The improved accuracy and efficiency of text detection benefit various sectors, reducing errors and enhancing user experiences.
- Multilingual Countries: In countries like India, where multiple languages are prevalent, this solution is particularly valuable. It simplifies interactions with sign boards that contain content in both national and regional languages.
In Conclusion:
Imagine you’re in a place where signs are written in different languages. Sometimes, these signs can have more than one language on them. This can make it confusing to understand what they say. But thanks to a smart computer program, we can now quickly figure out what’s written on these signs, even if they have different languages like English, Hindi, or Gujarati.
We used a special computer program called Faster RCNN, which is like a detective for signs. It’s really good at finding text on signboards. But to teach it, we needed to show it lots of signs. So, we made a special collection of signboard pictures to help the computer learn. We didn’t have these pictures before, so it was a bit like creating a new book.
The good news is that our program works really well. It can understand signs with different languages, and it can even tell you which language is used on the sign. This can be super helpful when you’re in a place with lots of different languages on signs, like in India.
So, in simple terms, our project helps people understand signs with different languages on them. It’s like having a helpful friend who can read signs for you, making it easier to get around and find the information you need.