Convolutional Bidirectional Variational Autoencoder for Image Domain
  Translation of Dotted Arabic Expiration
Ahmed Zidane Ghada Soliman
THIS paper proposes 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
Convolutional Recurrent Neural Network CRNN model to meet our specific
requirements and enhance its performance in our context, and then trained the
custom CRNN model with the filled-in images from the year of 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 systems for extracting the expiry dates
and sorting the products accordingly during the manufacture stage.
Additionally, it can overcome the manual entry of expiration dates that can be
time-consuming and inefficient at the merchants. Due to the lack of the
availability of the dotted Arabic expiration date images, we created an Arabic
dot-matrix True Type Font (TTF) for the generation of the synthetic images. We
trained the model with unrealistic synthetic dates of 59902 images and
performed the testing on a realistic synthetic date of 3287 images from the
year of 2019 to 2027, represented as yyyy/mm/dd. In our study, we demonstrated
the significance of latent bottleneck layer with improving the generalization
when the size is increased up to 1024 in downstream transfer learning tasks as
for image translation. The proposed approach achieved an accuracy of 97% on the
image translation with using the LCBVAE architecture that can be generalized
for any downstream learning tasks as for image translation and reconstruction.