Page Curl Research Articles

Document dewarping via text-line based optimization

This paper presents a new document image dewarping method that removes geometric distortions in camera-captured document images. The proposed method does not directly use the text-line which has been the most widely used feature for the document dewarping. Instead, we use the discrete representation of text-lines and text-blocks which are the sets of connected components. Also, we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively. With these distortion models and the discrete representation of the features, we design a cost function whose minimization yields the parameters of the distortion model. In the cost function, we encode the properties of the pages such as text-block alignment, line-spacing, and the straightness of text-lines. By describing the text features using the sets of discrete points, the cost function can be easily defined and efficiently solved by Levenberg–Marquadt algorithm. Experiments show that the proposed method works well for the various layouts and curved surfaces, and compares favorably with the conventional methods on the standard dataset.

A Survey on Document Image Processing Methods Useful for Assistive Technology for the Blind

This paper offers a review of the state-of-the-art document image processing methods and their classification by identifying new trends for automatic document processing and understanding. Document image processing (DIP) is an important problem related with most of the challenges coming from the image processing field and with applications to digital document summarization, readers for the visually impaired etc. Difficulties in the processing of documents can arise from lighting conditions, page curl, page rotation in 3D, and page layout segmentation. Document image processing is usually performed in the context of higher-level applications that require an undistorted document image such as optical character recognition and document restoration/preservation. Typically, assumptions are made to constrain the processing problem in the context of a particular application. In this survey, we categorize document image processing methods on the basis of the technique, provide detailed descriptions of representative methods in each category, and examine their pros and cons. It important to notice here that the DIP field is broad, thus we try to provide a top–down/horizontal survey rather a bottom up. At the same time, we target the area of document readers for the blind, and use this application to guide us in a top–down survey of DIP. Moreover, we present a comparative survey based on important aspects of a marketable system that is dependent on document image processing techniques.

Coupled snakelets for curled text-line segmentation from warped document images

Camera-captured, warped document images usually contain curled text-lines because of distortions caused by camera perspective view and page curl. Warped document images can be transformed into planar document images for improving optical character recognition accuracy and human readability using monocular dewarping techniques. Curled text-lines segmentation is a crucial initial step for most of the monocular dewarping techniques. Existing curled text-line segmentation approaches are sensitive to geometric and perspective distortions. In this paper, we introduce a novel curled text-line segmentation algorithm by adapting active contour (snake). Our algorithm performs text-line segmentation by estimating pairs of x-line and baseline. It estimates a local pair of x-line and baseline on each connected component by jointly tracing top and bottom points of neighboring connected components, and finally each group of overlapping pairs is considered as a segmented text-line. Our algorithm has achieved curled text-line segmentation accuracy of above 95% on the DFKI-I (CBDAR 2007 dewarping contest) dataset, which is significantly better than previously reported results on this dataset.

Building cameras for capturing documents

This paper explores those aspects of document capture that are specific to cameras. Each of them must be addressed in order to close the gap between taking a photograph of a document and capturing the document itself. We present results in five areas: (1) framing documents using structured light, (2) robustly dealing with ambient illumination when capturing glossy documents, (3) improving text quality when using mosaiced color sensors, (4) robustly and passively recovering perspective and image plane skew using text flow, and (5) measuring and undoing page curl using structured light and an applicable surface model. The ultimate success of subsequent document recognition will be heavily dependent on the successful completion of these tasks.