This library helps to evaluate the quality of document images before transferring them to the server.
npm install @sumsub/capture-sdk
import initCaptureSdk from '@sumsub/capture-sdk'
const captureSdk = await initCaptureSdk()
// returns numeric score between 0 and 1
// higher score means more unsatisfactory image
const score = await captureSdk.predictImageDataScore(imageData)
// returns true when image score less then maxAllowedScore (default value is 0.83), false if else
const result = await captureSdk.predictImageDataResult(imageData, maxAllowedScore)
This library helps evaluate the quality of document images before transferring them to the server.
The trained model is a lightweight version of SqueezeNet, weighing only 1 MB.
The classes are defined as follows:
- Class 1 consists of low-quality document photos and photos that are not from the document domain
- Class 0 comprises high-quality document photos
Train dataset:
- Class 1 contains 500k data collected by Sumsub, representing poor-quality document photos, and an additional 200k data from ImageNet that consists of photos not from the document domain
- Class 0 includes 500k data collected and generated by Sumsub, representing high-quality document photos
Test dataset:
- Class 1 has 100k data collected by Sumsub, which are poor-quality document photos that were rejected during the fastfail stage.
- Class 0 also has 100k data from Sumsub, representing high-quality document photos.
Metrics
- roc_auc_score = 0.85
- frtt_score(quantile=0.985) = 0.30 (threshold of 0.89)
- frtt_score(quantile=0.97) = 0.40 (threshold of 0.83)
frtt_score
To clarify, in the frtt_score metric, the quantile parameter determines the acceptable fraction of false positives that we set.
For example, when the quantile is 0.985, we expect our model to accurately classify 98.5% of Class 0 objects. There is a possibility of misclassifying (resulting in false positives) 1.5% of Class 0 objects.
Metrics such as Recall are then measured to determine the ratio of poor-quality photos (Class 1) captured at the selected threshold for the classifier.