An AI-powered OCR system for trading card data extraction, targeting Basic-EX and Mega-EX cards.

Project Description

ErieTCG OCR is designed to revolutionize how collectors and platforms manage trading card data. By utilizing advanced AI methodologies and state-of-the-art image processing, this application transforms visual card attributes into structured, searchable data. The service primarily targets Basic-EX and Mega-EX cards, capturing essential details such as card names, types, and numerical identifiers. With its modular architecture, the system offers robustness and flexibility, allowing seamless integration into existing workflows. Collectors, hobbyists, and related platforms benefit from streamlined data management, reduced manual input, and enhanced data accuracy. The application is built using Python, known for its rich libraries, contributing to high precision in object detection and classification. Deployed on a scalable cloud infrastructure, ErieTCG OCR ensures high uptime and responsiveness, making it an invaluable tool for trading card enthusiasts seeking efficiency and precision in data handling.

Scope of Work

The primary goal for the client was to automate the labor-intensive task of manually cataloging trading card data by converting it into a fully automated image-to-text processing pipeline. This required the development of a microservice-based backend capable of integrating a robust OCR engine. The pipeline needed to accurately identify and extract specific card attributes, which included card names, types, and numbers, from diverse card images. Another challenge was implementing image preprocessing techniques to enhance the accuracy of text extraction, alongside developing a custom Named Entity Recognition (NER) module to recognize and classify the different attributes of each card. The client also required the system to be deployable on a scalable cloud platform to accommodate fluctuating loads, ensuring the solution was not only precise but also fast and reliable under varying conditions.

Our Solution

Crazi Co delivered a comprehensive solution that addressed the client’s requirements by developing a sophisticated, end-to-end OCR pipeline. This involved implementing a modular architecture that supported easy updates and future enhancements. The OCR system was built using Python, leveraging its advanced libraries for image processing and object detection to achieve high accuracy levels. Key components of the solution included image preprocessing techniques for noise reduction, enhancing the quality of input images, and custom classifiers for card type differentiation. Additionally, a specialized Named Entity Recognition (NER) module was developed to extract and process relevant card attributes successfully. Crazi Co ensured the solution was deployable on a scalable cloud infrastructure, enabling the system to efficiently manage high volumes of data while maintaining optimal performance. The cloud deployment also facilitated easy scalability, allowing the system to handle peak loads seamlessly.

Key Features

• AI-Powered Image Processing: The application employs advanced AI techniques for image processing, improving data extraction accuracy from various trading card images. The automated preprocessing capabilities ensure only high-quality images make it to the OCR stage, reducing errors significantly.

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• Custom Named Entity Recognition (NER): A bespoke NER module was developed to identify and categorize trading card attributes precisely. This feature enhances the system's ability to classify card types, names, and numbers effectively, allowing collectors to access structured data with ease.

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• Cloud-Integrated Deployment: The OCR system is deployed on a scalable cloud platform, providing resilient and fast processing capabilities. This infrastructure supports high availability and ensures the application can handle varying data loads efficiently.

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