A Deep Transfer Learning Approach to Document Image Quality Assessment Document image quality assessment (DIQA) is an important process for various applications such as optical character recognition (OCR) and document restoration. In this paper we propose a noreference DIQA model based on a deep convolutional neural network (DCNN), where the rich knowledge of natural scene image characterization of a previouslytrained DCNN is exploited towards OCR accuracy oriented document image quality assessment. Following a twostage deep transfer learning procedure, we finetune the knowledge base of the DCNN in the first phase and bring in a taskspecific segment consisting of three fully connected (FC) layers in the second phase. Based on the finetuned knowledge base, the taskspecific segment is trained from scratch to facilitate the application of the transferred knowledge on the new task of document quality assessment. Testing results on a benchmark dataset demonstrate that the proposed model achieves stateoftheart performance.

A New Document Image Quality Assessment Method Based on Hast Derivations With the rapid emergence of new technologies, a voluminous number of images including document images is generated every day. Considering the volume of data and complexity of processes, manual analysis, annotation, recognition, classification, and retrieval, of such document images is impossible. To automatically deal with such processes, many document image analysis applications exist in the literature and many of them are currently in place in different organisation and institutes. The performance of those applications are directly affected by the quality of document images. Therefore, a document image quality assessment (DIQA) method is of primary need to allow users capture, compress and forward good quality (readable) document images to various information systems, such as online business and insurance, for further processing. To assess the quality of document images, this paper proposes a new fullreference DIQA method using first followed by second order Hast derivations. A similarity map is then created using second order Hast derivation maps obtained by employing Hast filters on both reference and distorted images. An average pooling is then employed to obtain a quality score for the distorted document image. To evaluate the proposed method, two different datasets were used. Both datasets are composed of images with the mean human opinion scores (MHOS) considered as ground truth. The results obtained from the proposed DIQA method are superior to the results reported in the literature.