CER(character error rate) increases while decrease in validation loss

I am training an OCR model with resnet as backbone and vanilla Transformer as decoder on the Rimes dataset. I tried the following experiments and got these results:

without Augmentation: best validation loss==> 111, best training loss==>2.77 ,Test CER==> 5.75

with Augmentation: best validation loss==> 71, best training loss==>95 ,Test CER==> 10.2

Loss= KL divergence loss with label smoothing in both cases.

Augmentation function:

                albumentations.MotionBlur(p=1, blur_limit=5),
                albumentations.OpticalDistortion(p=1, distort_limit=0.05),
                albumentations.GaussNoise(p=1, var_limit=(10.0, 100.0)),
                albumentations.RandomBrightnessContrast(p=1, brightness_limit=0.2),
                albumentations.Downscale(p=1, scale_min=0.3, scale_max=0.5),


I am very confused why validation loss decreased and CER increased. It is happening with Rimes dataset only while on other datasets like IAM and Washington there is no such issue.