Light-weight Document Image Cleanup using Perceptual Loss
This work addresses the need for efficient document image cleanup in embedded applications, offering a practical solution for users with memory and latency constraints, though it is incremental as it builds on existing CNN and perceptual loss techniques.
The paper tackles the problem of cleaning up degraded document images on resource-constrained devices like smartphones by proposing a lightweight encoder-decoder CNN with perceptual loss, achieving models 65-1030 times smaller in parameters and 3-27 times smaller in operations compared to existing state-of-the-art methods.
Smartphones have enabled effortless capturing and sharing of documents in digital form. The documents, however, often undergo various types of degradation due to aging, stains, or shortcoming of capturing environment such as shadow, non-uniform lighting, etc., which reduces the comprehensibility of the document images. In this work, we consider the problem of document image cleanup on embedded applications such as smartphone apps, which usually have memory, energy, and latency limitations due to the device and/or for best human user experience. We propose a light-weight encoder decoder based convolutional neural network architecture for removing the noisy elements from document images. To compensate for generalization performance with a low network capacity, we incorporate the perceptual loss for knowledge transfer from pre-trained deep CNN network in our loss function. In terms of the number of parameters and product-sum operations, our models are 65-1030 and 3-27 times, respectively, smaller than existing state-of-the-art document enhancement models. Overall, the proposed models offer a favorable resource versus accuracy trade-off and we empirically illustrate the efficacy of our approach on several real-world benchmark datasets.