We propose an approach of Ladder Bottom-up Convolutional Bidirectional Variational Autoencoder (LCBVAE) architecture for the encoder and decoder, which is trained on the image translation of the dotted Arabic expiration dates by reconstructing the Arabic dotted expiration dates into filled-in expiration dates. We employed a customized and adapted version of the convolutional recurrent neural network (CRNN) model to meet our specific requirements and enhance its performance in our context, and then we trained the custom CRNN model with the filled-in images from the year 2019 to 2027 to extract the expiration dates and assess the model performance of LCBVAE on the expiration date recognition. The pipeline of (LCBVAE + CRNN) can be then integrated into an automated sorting system for extracting the expiry dates and sorting the products accordingly during the manufacturing stage. In addition, it can overcome the manual entry of expiration dates that can be time-consuming and inefficient at the merchants. Due to the lack of available dotted Arabic expiration date images, we created an Arabic dot-matrix TrueType font for the generation of the synthetic images. We trained the model with unrealistic synthetic dates of 60,000 images and performed the testing on a realistic synthetic date of 3000 images from the year 2019 to 2027, represented as yyyy/mm/dd. We demonstrated the significance of the latent bottleneck layer by improving the generalization when the size is increased up to 1024 in downstream transfer learning tasks for image translation. The proposed approach achieved an accuracy of 97% on the image translation using the LCBVAE architecture that can be generalized for any downstream learning tasks for image translation and reconstruction.