AI Image Data Extraction

Fragrance
Daten wissen case study image

Business Impact

Streamlined Design Boosts Efficiency and Productivity.

Customer Facts

Location: India

Industry: Fragrance

Daten wissen case study image

Problem Statement

Problems we Acquired

Before the implementation of AI Image Data Extraction, the process of extracting and recording data from fragrance product images was done manually. This involved users checking images and gathering details, which was time-consuming and prone to human errors. The manual method also resulted in inconsistencies and inefficiencies in data gathering, impacting the accuracy and reliability of the data collected.

Challenges

Obstacles we faced

  • Blurry images are a significant obstacle in fragrance product recognition. Images captured in uncontrolled environments often suffer from motion blur, obscuring essential details necessary for accurate product identification and classification.
  • Images with inappropriate content or unsuitable angles, such as poor lighting, occlusions, or distracting backgrounds, can hinder accurate recognition.
  • Locating and recognizing brand names and logos within images can be challenging, especially when they are small, partially obscured, or stylized in a unique font.

Technologies

Tech stack we used

Technology use

Python

Technology use

React JS

Technology use

LLM

Technology use

OCR

Technology use

NLP

Technology use

Django

Solution

How we made end solution

  • Gaining a thorough understanding of how text and data are presented on fragrance covers, labels, or packaging. This involves studying various formats, fonts, and layouts used in the fragrance industry to ensure accurate text extraction.
  • Creating data in a structured manner by using OCR to extract text from fragrance images.
  • Applying NLP to convert the raw text data into a structured format suitable for further processing. This includes categorizing and tagging extracted information, such as product s name, parameters, and other relevant details.
  • Developing the OCR system to accurately scan and extract text data from a variety of fragrance images.
  • Leveraging LLM to enhance the understanding and interpretation of the extracted text, ensuring high accuracy and reducing errors.
  • Integrating the OCR, NLP, and LLM technologies into a seamless workflow that automates the entire data extraction and processing pipeline.
  • Ensuring that the extracted data is accurately recorded and structured in a way that meets the needs of the users.
  • Creating a user-friendly interface that allows users to upload fragrance images and receive the extracted data in the required form.
  • Developing a dashboard that displays the extracted data and provides options for users to review and validate the information, ensuring it meets their requirements.

Result

Outcome we get

  • By automating data extraction from fragrance product images, the system reduces manual effort and accelerates the process, allowing users to handle larger volumes of data quickly and accurately.
  • Utilizing OCR, NLP, and LLM technologies ensures precise extraction and structuring of data, minimizing errors that were common in manual methods. This leads to reliable data that can be confidently used for analysis and decision-making.
  • The user-friendly interface and dashboard simplify the review and validation of extracted data, enhancing workflow efficiency and ensuring that data meets specific requirements seamlessly.
  • Reduced reliance on manual labor and improved operational efficiency translate into cost savings for organizations, making operations more cost-effective and scalable.
  • By extracting detailed information such as product names, parameters, and other relevant details, the system empowers users to derive actionable insights from their data, supporting better strategic decisions and product management.

Let us Transform Your Business

Industries

Quick Links

Domain

Newsletter

Get in Touch With Us

  • +91 9820080751
  • +91 7021980537
  • +91 7977690595 (Job Queries)
  • contact.us@datenwissen.com

What our Team says

Daten & Wissen Glassdoor

Featured On

© Daten & Wissen. All Rights Reserved.

Privacy Policy

Disclaimer

Cookies Policy