Automated Label Text Extraction System for Consumer Product Databases

The client faces bottlenecks due to manual transcription of ingredient lists from product labels, which are often circular in shape with small fonts and complex color contrasts. This process is time-consuming, error-prone, and hampers timely updates to their product safety database, especially given the vast and rapidly changing market of beauty products. Additionally, ingredients are listed in multiple languages, complicating data consistency and analysis.

A non-profit organization dedicated to testing and informing consumers about the safety and transparency of beauty and personal care products through data collection and analysis.

• Develop an internal application to automate the extraction of text from product labels, supporting images captured in multiple languages and varying resolutions.
• Reduce manual data entry time significantly, enabling faster database updates and improving data accuracy.
• Support diverse product shapes and label designs, including circular packaging and complex contrast scenarios.
• Implement a scalable technology solution that can adapt to increasing product volumes and expanding language requirements.
• Provide real-time feedback and usability for staff to ensure quick adoption and iterative improvement.

• Image capture module capable of handling various product shapes, including circular labels.
• Preprocessing functions to enhance image quality, contrast, and resolution for accurate text recognition.
• Multi-language support including recognition of 25+ languages commonly used in product labels.
• Text extraction and transcription feature leveraging Optical Character Recognition (OCR) techniques, optimized for small fonts and complex backgrounds.
• Integration with existing product database to automate data entry and updates.
• User interface for review and correction of extracted data prior to database entry.
• Secure storage of images and extracted data, possibly utilizing cloud storage solutions such as cloud buckets.

• Product database systems for automated data population
• Cloud storage services for images and processed data
• Existing internal tools for user authentication and feedback collection

• High accuracy in text recognition with a minimum of 95% precision
• Support for image processing of various resolutions and formats
• Multilingual support for at least 25 languages
• System scalability to handle increasing product image volume
• Data security measures compliant with privacy standards
• User-friendly interface with rapid response times

The implementation of the automated label text extraction system aims to significantly reduce manual data entry time, increase data accuracy, and accelerate database updates. This will enable the organization to provide more timely and reliable product safety information, support faster internal workflows, and enhance consumer education efforts. Targeted improvements include a reduction in data transcription time by over 50%, quicker product database refresh cycles, and improved data consistency across multiple languages and product types.