Anjaneyulu Seetha Rama Kuchibhotla

Detecting modifications in paper documents: a coding approach

This paper presents an algorithm called CIPDEC (Content Integrity of Printed Documents using Error Correction), which identifies any modifications made to a printed document. CIPDEC uses an error correcting code for accurate detection of addition/deletion of even a few pixels. A unique advantage of CIPDEC is that it works blind - it does not require the original document for such detection. Instead, it uses fiducial marks and error correcting code parities. CIPDEC is also robust to paper-world artifacts like photocopying, annotations, stains, folds, tears and staples. Furthermore, by working at a pixel level, CIPDEC is independent of language, font, software, and graphics that are used to create paper documents. As a result, any changes made to a printed document can be detected long after the software, font, and graphics have fallen out of use. The utility of CIPDEC is illustrated in the context of tamper-proofing of printed documents and ink extraction for form-filling applications.

Real-time optimal-memory image rotation for embedded systems

Skew-corrected document images are necessary for subsequent downstream operations such as archiving, printing or improving OCR performance. Image rotation is a necessary and more expensive step in achieving skew correction of document images. Other applications of rotation include, image registration and orientation correction. Traditional image rotation algorithms [2-4] such as three-shear rotation [1] require three separable shears of the image. The embedded use of such techniques in scanners/printers presents technical challenges, since the memory available is limited and/or the document image is only available progressively in chunks of say 32 or 64 rows (swaths). Traditional image rotation algorithms require the entire image to be available before commencing the rotation operation. This paper presents an approach that allows image rotation using swaths of the image thus minimizing the overall memory requirement. We theoretically prove that the number of image swaths that are to be buffered is independent of the image size and depends only on the rotation angle. This approach enables rotation of any arbitrary sized image on memory constrained devices. The memory savings realized is at least 80%, for an A4-sized document image rotated 15°. Our progressive approach demonstrates real-time image rotation and hence improves on the state-of-the-art approaches for reduction of rotation complexity [5-10].

Real-time embedded skew detection and frame removal

It is common to observe document skew and frame artifacts while photocopying and scanning documents. The motivation of this work is to embed skew correction and frame removal in the copy pipeline of a device to achieve ‘one touch’ cleanup. The two challenges that this poses are the need for: (a) substantially reducing computation and memory requirements and (b) minimizing the false positives. Peripheral document features, such as, page/content edges are low-complexity document skew predictors, and content-based approaches are of relatively higher complexity skew predictors. But state-of-the-art page edge detection methods fail on low-contrast document images, or for similar scanbed/document background. To minimize false positives required in embedded implementations, we propose: (1) a robust page edge detection algorithm that is a multiplicative combination of gradients and line based page edge detectors, (2) a robust skew detection algorithm that is a linear combination of page/content edge and content based predictors, and (3) a pipeline for skew correction and frame removal that uses these algorithms and has near-100% accuracy over a wide range of document images.

Paper widgets: Visually aesthetic “smarts” for document images

Traditional 1Dand 2D barcodes provide high data density, but they are visually jarring and require isolated whitemargins for placement. In this work, we introduce new machine-readable “smarts” for paper documents, called paper widgets. Like barcodes, paper widgets contain digital data which can be read upon scanning and decoding. However, unlike barcodes, they have very small footprint (fraction of sq.cm.), and carry a human-readable component that provides visual meaning. Furthermore, paper widgets can be placed and recovered in a distributed fashion from any position on a document image. For example, they can be positioned right beside contents of interest, and need not be confined to isolated white margins. We describe how all these widget properties can be simultaneously achieved using simple operations that are robust to print-scan distortions. In particular, we highlight a novel constrained coding technique that helps combat print-scan inter-symbol interference (ISI), and a Gabor-filtering based extraction that accurately identifies widget regions from any position on a scanned document image. Experimental evaluations reveal that paper widgets can be recovered from print-scan distortions with near-100% accuracy.