Automated dotted arabic expiration date extraction using optimized convolutional autoencoder and custom CRNN

Abstract In this research, we presented a novel method for extracting dotted Arabic expiration dates automatically. Our approach utilizes an optimized convolutional autoencoder combined with a bidirectional to reconstruct Arabic dot-matrix expiration dates into their corresponding filled-in formats. To recognize these dates, we customized a lightweight convolutional recurrent neural network. Due to the unavailability of a suitable dataset for Arabic dot-matrix expiration dates, we addressed this gap by generating synthetic images using a custom-designed Arabic dot-matrix True Type Font. Our model was trained on a synthetic dataset comprising 3,287 images, spanning the years 2019 to 2027. We evaluated our model performance in recognizing expiration dates from the reconstructed images. The proposed system achieved a test accuracy of 99.4% on a dataset of 658 images. Additionally, the image reconstruction process attained a Structural Similarity Index score of 0.46. This method can also be applied to other tasks, such as image translation. Furthermore, the pipeline can be integrated into automated manufacturing systems to identify and sort products based on expiration dates, eliminating the inefficiencies of manual data entry. By improving both accuracy and efficiency, this solution offers a practical advancement for recognizing Arabic dot-matrix expiration dates in industrial settings.