Automated Image Quality Check

Approach and Solution

We decided to tackle this problem in two parts:

• Image quality identification
• Image subject identification

Image quality identification

• By creating a machine learning model, we were able to identify whether an image is blurry or not almost instantly. The model takes in various attributes of an image and based on subjective evaluations already done, we trained a model to classify images as blurred, unsure, or good. The unsure images can again be subjected to human evaluation for confirmation. This approach largely reduces the number of images to be manually checked.

Image subject identification

• Next, we created a web application that identified and labeled images according to their subject matter using google vision API.
• Through machine learning and flask, we were able to create an application that can identify the quality of an image and also identify and label the subject matter present in the image.

Benefits

• Two in one solution (Image quality and subject matter).
• Automated quality control is achieved through machine learning.
• Saved 90% of Manual inspection time through automation.
• Segregation of good quality images helped in performing next-level tasks like OCR and Object detection with better accuracy.