Card Vision
The experts at DivedeepAI developed Card Vision, a smart computer vision operated application that is used to determine the condition, authenticity of a Pokemon Card.
About Client
A stealth startup focused on leveraging Computer Vision to solve real world problems.
The Challenge
The main Challenge was data collection and information extraction. There was no subgrades (edges, surface etc.) information available with cards on the ebay website so we had to manually write up all this information for Models’ training. If we did not do this, we would not be able to predict card conditions. Moreover, we had to crop and preprocess all images because these cards were laminated and we had to preprocess these for text and other features extraction such as damage detection, border thickness check etc.
The Solution
We used deep learning model ResNet for training and prediction of these subgrade values to determine card condition. After completing data training, We saved the trained ResNet model for future use to predict a card’s grade, corner, edges, centering and surface condition. To determine the authenticity of a pokemon card we implemented a check of 8 factors using different python modules such as openCV, Pytesseract, and numpy etc.
The Impact
The application checks for these features in each card and tells which tests pass and which one fails. If all 8 factors pass the checks then the card is real. Even if one check fails, we call it an error card.
HIGHLIGHTS
- Indisputable provenance of authenticity
- Assessment of card's condition
- Detection of man-made defects
- Identifying card's worth
- Provide Digital Assistance
- Ensure faster decisions
Project in Action
Preprocessing Data
Training ResNet
Grade Prediction
Condition Assessment
Client Testimonial
Amazing Dev! Do not hesitate to hire! Finished the job promptly and made sure all my needs were met!