Unique Image Recognition App To Solve Tagging Woes In Bulk Images
About
The client is a leading and largest news publishing company in India, with a strong foundation in the media and entertainment industry. In addition to print media, the company has a reputable presence in various media platforms such as FM radio, women's magazines, a financial newspaper, an English film magazine, an entertainment channel, and most notably, a prominent news channel in India.
The client understands the importance of image recognition and the hassles that come along with it. Brainvire has created an app called ‘TAGGER’ which eliminates the problems associated with such tasks. This app is compatible with the Microsoft desktops and can handle large amounts of data at once. This app makes the end-users job easier and allows the management of metadata related to the images.
Project Highlights
Brainvire is at the forefront when it comes to unique and creative tasks. Our brilliant web application development team has tried to deliver an image recognition app and have tried our best to eliminate the tagging issue. Brainvire has created an app called ‘TAGGER’ which eliminates the problems associated with such tasks. This app is compatible with the Microsoft desktops and can handle large amounts of data at once. This app makes the end-users job easier and allows the management of metadata related to the images. It also allows users to create tags and organize data into different categories. It also provides a feature of auto tagging which can save time and effort of the users. We used machine learning algorithms to analyze the visual content of an image and identify objects in background , people faces, sentiments, and other relevant elements within it. The software can automatically tag images with descriptive labels based on the visual content of the images, and also identify and extract information about faces in the images, such as facial features, expressions, and demographics.
The Challenges
- Image Pre-processing:Before tagging images, We had to pre-processed to ensure the tagging process is accurate and efficient.
- Data Processing:Bulk image tagging required processing large amounts of data which can was challenging
- Face Recognition:The clients cannot upload data or metadata all at once in the app. This has resulted in incorrect recognition and correction is expensive.
- Automate Image Tagging Process:Manual tagging of the images was not possible since the organization had a large amount of data, we had to automate this process.
Tech Stack
.Net core
Azure cloud
jQuery
Microsoft Cognitive Services
Microsoft SQL Server
Python
Result
Feature Extraction:
Pre-processing helped in extracting features from an image, such as edges, corners, and shapes. This was useful for object recognition and classification.Scalability:
Data processing allowed us to quickly and efficiently process large volumes of data, making it easier to analyze and act on the results.Precise recognition
With better software, this app recognizes images quicker and more accurately than before.Increased Efficiency
Automated image tagging eliminated the potential for human error and it easier to quickly locate images within a large database. This saved time and money, as it eliminated the need for manual sorting and tagging.
