Efficient Generative Adversarial Networks for Color Document Image Enhancement and Binarization Using Multi-scale Feature Extraction

The outcome of text recognition for degraded color documents is often unsatisfactory due to interference from various contaminants. To extract information more efficiently for text recognition, document image enhancement and binarization are often employed as preliminary steps in document analysis. Training independent generative adversarial networks (GANs) for each color channel can generate images where shadows and noise are effectively removed, which subsequently allows for efficient text information extraction. However, employing multiple GANs for different color channels requires long training and inference times. To reduce both the training and inference times of these preliminary steps, we propose an efficient method based on multi-scale feature extraction, which incorporates Haar wavelet transformation and normalization to process document images before submitting them to GANs for training. Experiment results show that our proposed method significantly reduces both the training and inference times while maintaining comparable performances when benchmarked against the state-of-the-art methods. In the best case scenario, a reduction of 10% and 26% are observed for training and inference times, respectively, while maintaining the model performance at 73.79 of Average-Score metric. The implementation of this work is available atthis https URL.