Here we present an evaluation of an ideal document acquisition guidance system. Guidance is provided to help someone take a picture of a document capable of Optical Character Recognition (OCR). Our method infers the pose of the camera by detecting a pattern of fiduciary markers on a printed page. The guidance system offers a corrective trajectory based on the current pose, by optimizing the requirements for complete OCR. We evaluate the effectiveness of our software by measuring the quality of the image captured when we vary the experimental setting. After completing a user study with eight participants, we found that our guidance system is effective at helping the user position the phone in such a way that a compliant image is captured. This is based on an evaluation of a one way analysis of variance comparing the percentage of successful trials in each experimental setting. Negative Helmert Contrast is applied in order to tolerate only one ordering of experimental settings: no guidance (control), just confirmation, and full guidance.

Mobile optical character recognition (OCR) apps have come of age. Many blind individuals use them on a daily basis. The usability of such tools, however, is limited by the requirement that a good picture of the text to be read must be taken, something that is difficult to do without sight. Some mobile OCR apps already implement auto-shot and guidance mechanisms to facilitate this task. In this paper, we describe two experiments with blind participants, who tested these two interactive mechanisms on a customized iPhone implementation. These experiments bring to light a number of interesting aspects of accessing a printed document without sight, and enable a comparative analysis of the available interaction modalities.

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Abstract-Scholarly content needs to be online, and for much mass produced content, that migration has already happened. Unfortunately, the online presence of scholarly content is much more sporadic for long tail material such as small journals, original source materials in the humanities and social sciences, non-journal periodicals, and more. A large barrier to this content being available is the cost and complexity of setting up a digitization project for small and scattered collections coupled with a lack of revenue opportunities to recoup those costs. Collections with limited audiences and hence limited revenue opportunities are nonetheless often of considerable scholarly importance within their domains. The expense and difficulty of digitization presents a significant obstacle to making such paper archives available online. To address this problem, the Decapod project aims at providing a solution that is primarily suitable for small to medium paper archives with material that is rare or unique and is of sufficient interest that it warrants being made more widely available. This paper gives an overview of the project and presents its current status.

Abstract-This paper describes a system for capturing images of books with a handheld 3D stereo camera, which performs dewarping to produce images that are flattened. A Fujifilm consumer grade 3D camera provides a highly mobile and low-cost 3D capture device. Applying standard computer vision algorithms, camera calibration is performed, the captured images are stereo rectified, and the depth information is computed by block matching. Due to technical limitations, the resulting point cloud has defects such as splotches and noise, which make it hard to recover the precise 3D locations of the points on the book pages. We address this problem by computing curve profiles of the depth map and using them to build a cylinder model of the pages. We then employ meshes to facilitate the flattening and rendering of the cylinder model in virtual space. We have implemented a prototype of the system and report on a preliminary evaluation based on measuring the straightness of resulting text lines.