Creating uniform lighting for archival-quality document acquisition remains a non-trivial problem. We propose a novel method for automatic photometric correction of non-planar documents by estimating a single, point light-source using a simple light probe. By adding a simple piece of folded white paper with a known 3D surface to a scene, we are able to extract the 3D position of a light source, automatically perform white balance correction, and determine areas of poor illumination. Furthermore, this method is designed with the purpose of adding it to an already implemented document digitization pipeline. To justify our claims, we provide an accuracy analysis of our correction technique using simulated ground-truth data which allows individual sources of error to be determined and compared. These techniques are then applied on real documents that have been acquired using a 3D scanner.

This paper presents a framework to restore the 2D content printed on documents in the presence of geometric distortion and non-uniform illumination. Compared with textbased document imaging approaches that correct distortion to a level necessary to obtain sufficiently readable text or to facilitate optical character recognition (OCR), our work targets nontextual documents where the original printed content is desired. To achieve this goal, our framework acquires a 3D scan of the document's surface together with a high-resolution image. Conformal mapping is used to rectify geometric distortion by mapping the 3D surface back to a plane while minimizing angular distortion. This conformal "deskewing" assumes no parametric model of the document's surface and is suitable for arbitrary distortions. Illumination correction is performed by using the 3D shape to distinguish content gradient edges from illumination gradient edges in the high-resolution image. Integration is performed using only the content edges to obtain a reflectance image with significantly less illumination artifacts. This approach makes no assumptions about light sources and their positions. The results from the geometric and photometric correction are combined to produce the final output.

This paper introduces a method for building a readable image of an opaque, rolled or folded text from a volumetric, penetrating scan. The problem is framed by localizing, constructing, and manipulating an image induced by a surface embedded in a 3D voxel space. There are two central contributions that lead to the demonstrated results. First is an energy-based texture formation algorithm, which is a function of voxel intensities and the geometry of the embedded surface. Second is a regularization algorithm based on a constrained mapping of the embedded surface to a regularized image plane. The mapping preserves angles and lengths, which minimizes the distortion of text in the image. The experimental results show readable images derived from custom, high resolution (X-ray-based) CT scans of rolled papyrus and ink samples. These methods are significant for scholars seeking to study inaccessible texts, and may lead to viable techniques for scanning everyday opaque objects (books) without opening them.

We present a system to restore the 2D content printed on distorted documents. Our system works by acquiring a 3D scan of the document's surface together with a high-resolution image. Using the 3D surface information and the 2D image, we can ameliorate unwanted surface distortion and effects from non-uniform illumination. Our system can process arbitrary geometric distortions, not requiring any pre-assumed parametric models for the document's geometry. The illumination correction uses the 3D shape to distinguish content edges from illumination edges to recover the 2D content's reflectance image while making no assumptions about light sources and their positions. Results are shown for real objects, demonstrating a complete framework capable of restoring geometric and photometric artifacts on distorted documents