This page will help you understand the diff Visualizer component of the OCR Layout tool.

Ground Truth vs Model

In the OCR setting, our goal is to train our models such that the predicted text is as close to the ground truth. Sometimes, it might be useful to visualize the exact characters that have been wrongly predicted by our models. To do so the OCR tool has provided a diff visualizer.

The gif below should help understand how to use the feature.

Gif displaying how to change the image highlight color

Model Truth vs Model

When you have outputs from multiple models, you may want to see the difference of two strings between those two models. The OCR layout provides a mechanism to do so.

Gif displaying how to change the image highlight color


The above visualization, depicts the minimal operations required to convert the target string to the reference string. Notice that every distinct color bounded-text is also annotated with numbers. Also notice that for a color bounded component in the reference string with annotation i, there could be a corresponding color bounded text annotated with the same number i in the target string. (the color of these two color-bounded texts need not be the same).

Color of reference Component i Color of Target Component i What does it Mean?
‘<some-reference-text>(i)’ ‘<some-target-text>(i)’ Component i of reference and component i of the target are Equal
‘<some-reference-text>(i)’ non-existent Insert Component i of reference after component i-1 of target
non-existent ‘<some-target-text>(i)’ Delete Component i of target
‘<some-reference-text>(i)’ ‘<some-target-text>(i)’ Replace component i of target with component i of reference