Effective Geometric Restoration of Distorted Historical Document for Large-Scale Digitization

Due to storage conditions and material’s non-planar shape, geometric distortion of the 2-D content is widely present in scanned document images. Effective geometric restoration of these distorted document images considerably increases character recognition rate in large-scale digitisation. For large-scale digitisation of historical books, geometric restoration solutions expect to be accurate, generic, robust, unsupervised and reversible. However, most methods in the literature concentrate on improving restoration accuracy for specific distortion effect, but not their applicability in large-scale digitisation. This paper proposes an effective mesh based geometric restoration system, (GRLSD), for large-scale distorted historical document digitisation. In this system, an automatic mesh generation based dewarping tool is proposed to geometrically model and correct arbitrary warping historical documents. An XML based mesh recorder is proposed to record the mesh of distortion information for reversible use. A graphic user interface toolkit is designed to visually display and manually manipulate the mesh for improving geometric restoration accuracy. Experimental results show that the proposed automatic dewarping approach efficiently corrects arbitrarily warped historical documents, with an improved performance over several state-of-the-art geometric restoration methods. By using XML mesh recorder and GUI toolkit, the GRLSD system greatly aids users to flexibly monitor and correct ambiguous points of mesh for the prevention of damaging historical document images without distortions in large-scale digitalisation

Innovative Techniques for Digitizing and Restoring Deteriorated Historical Documents

Recent large-scale document digitization initiatives have created new modes of access to modern library collections with the development of new hardware and software technologies. Most commonly, these digitization projects focus on accurately scanning bound texts, some reaching an efficiency of more than one million volumes per year. While vast digital collections are changing the way users access texts, current scanning paradigms can not handle many non-standard materials. Documentation forms such as manuscripts, scrolls, codices, deteriorated film, epigraphy, and rock art all hold a wealth of human knowledge in physical forms not accessible by standard book scanning technologies. This great omission motivates the development of new technology, presented by this thesis, that is not-only effective with deteriorated bound works, damaged manuscripts, and disintegrating photonegatives but also easily utilized by non-technical staff. First, a novel point light source calibration technique is presented that can be performed by library staff. Then, a photometric correction technique which uses known illumination and surface properties to remove shading distortions in deteriorated document images can be automatically applied. To complete the restoration process, a geometric correction is applied. Also unique to this work is the development of an image-based uncalibrated document scanner that utilizes the transmissivity of document substrates. This scanner extracts intrinsic document color information from one or both sides of a document. Simultaneously, the document shape is estimated to obtain distortion information. Lastly, this thesis provides a restoration framework for damaged photographic negatives that corrects photometric and geometric distortions. Current restoration techniques for the discussed form of negatives require physical manipulation to the photograph. The novel acquisition and restoration system presented here provides the first known solution to digitize and restore deteriorated photographic negatives without damaging the original negative in any way. This thesis work develops new methods of document scanning and restoration suitable for wide-scale deployment. By creating easy to access technologies, library staff can implement their own scanning initiatives and large-scale scanning projects can expand their current document-sets

Document image restoration - For document images scanned from bound volumes -

Ph.DDOCTOR OF PHILOSOPH

A unified framework for document image restoration

Ph.DDOCTOR OF PHILOSOPH

DocScanner: Robust Document Image Rectification with Progressive Learning

Compared with flatbed scanners, portable smartphones are much more convenient for physical documents digitizing. However, such digitized documents are often distorted due to uncontrolled physical deformations, camera positions, and illumination variations. To this end, we present DocScanner, a novel framework for document image rectification. Different from existing methods, DocScanner addresses this issue by introducing a progressive learning mechanism. Specifically, DocScanner maintains a single estimate of the rectified image, which is progressively corrected with a recurrent architecture. The iterative refinements make DocScanner converge to a robust and superior performance, while the lightweight recurrent architecture ensures the running efficiency. In addition, before the above rectification process, observing the corrupted rectified boundaries existing in prior works, DocScanner exploits a document localization module to explicitly segment the foreground document from the cluttered background environments. To further improve the rectification quality, based on the geometric priori between the distorted and the rectified images, a geometric regularization is introduced during training to further improve the performance. Extensive experiments are conducted on the Doc3D dataset and the DocUNet Benchmark dataset, and the quantitative and qualitative evaluation results verify the effectiveness of DocScanner, which outperforms previous methods on OCR accuracy, image similarity, and our proposed distortion metric by a considerable margin. Furthermore, our DocScanner shows the highest efficiency in runtime latency and model size

MataDoc: Margin and Text Aware Document Dewarping for Arbitrary Boundary

Document dewarping from a distorted camera-captured image is of great value for OCR and document understanding. The document boundary plays an important role which is more evident than the inner region in document dewarping. Current learning-based methods mainly focus on complete boundary cases, leading to poor document correction performance of documents with incomplete boundaries. In contrast to these methods, this paper proposes MataDoc, the first method focusing on arbitrary boundary document dewarping with margin and text aware regularizations. Specifically, we design the margin regularization by explicitly considering background consistency to enhance boundary perception. Moreover, we introduce word position consistency to keep text lines straight in rectified document images. To produce a comprehensive evaluation of MataDoc, we propose a novel benchmark ArbDoc, mainly consisting of document images with arbitrary boundaries in four typical scenarios. Extensive experiments confirm the superiority of MataDoc with consideration for the incomplete boundary on ArbDoc and also demonstrate the effectiveness of the proposed method on DocUNet, DIR300, and WarpDoc datasets.Comment: 12 page