Print-Scan Resilient Text Image Watermarking Based on Stroke Direction Modulation for Chinese Document Authentication

Print-scan resilient watermarking has emerged as an attractive way for document security. This paper proposes an stroke direction modulation technique for watermarking in Chinese text images. The watermark produced by the idea offers robustness to print-photocopy-scan, yet provides relatively high embedding capacity without losing the transparency. During the embedding phase, the angle of rotatable strokes are quantized to embed the bits. This requires several stages of preprocessing, including stroke generation, junction searching, rotatable stroke decision and character partition. Moreover, shuffling is applied to equalize the uneven embedding capacity. For the data detection, denoising and deskewing mechanisms are used to compensate for the distortions induced by hardcopy. Experimental results show that our technique attains high detection accuracy against distortions resulting from print-scan operations, good quality photocopies and benign attacks in accord with the future goal of soft authentication

METRICS AND NEATENING OF HANDWRITTEN CHARACTERS

At the present time, with the technology that many portable devices have, pen-based input is widely supported and input can be done through handwriting rather than just by typing. While character recognition and other input issues are the main consideration with these devices, there are many interesting output issues related to digital ink. This thesis investigates the questions of handwriting neatening and personal handwriting fonts. We use the notion of “character metrics”, .which refer to the location and size of various features, such as ascender height and character width. Recording these features for handwritten characters allows for input and output transformations and may help with recognition in a 2-dimensional setting such as for mathematical handwriting recognition. To be able to create personalized handwriting fonts, we have constructed a tool to collect samples where lines related to the characters metrics can be recorded. Handwriting always comes with variations in alignment and size so certain measurements based on these metrics can be extracted to perform the desired transformations to obtain a set of samples in a normalized form

A model for automatic optical scaling of type designs for conventional and digital technology

In the history of type design, two methods have been used to scale type-- to produce enlarged or reduced letterforms from a reference size. With original handcut fonts, designers performed optical scaling (scaling by eye) that varied the proportions of letterform features over a range of sizes in a nonlinear manner. That is, letterform feature proportions were size dependent. This was an entirely manual and intuitive process. More recently, however, the use of the lens, as well as computational and other technologies, has allowed letterforms to be scaled automatically from a reference character, a simple proportional enlargement or reduction. To date, little work has been done to combine these two methods, that is to say, to automatically perform nonlinear scaling of a reference character in order to approximate the optical scaling performed by skilled type designers and punchcutters. This research developed a mathematical model of optical scale in type design, consisting of two parts: (1) a model of the scaling of individual letterform features; and (2) a model of the scaling of entire letterforms. The model was tested by applying it to the original handcut fonts that supplied the initial data for the research in order to generate synthetic letterforms. These nonlinear synthetic letterforms were then compared with the originals, as well with proportionally scaled letterforms generated by the model approximated the original optically scaled handcut letterforms. In addition, the performance of the proportionally scaled letterforms was compared with the originals, as well as with the nonlinear, synthetic forms

A Font Search Engine for Large Font Databases

A search engine for font recognition is presented and evaluated. The intended usage is the search in very large font databases. The input to the search engine is an image of a text line, and the output is the name of the font used when rendering the text. After pre-processing and segmentation of the input image, a local approach is used, where features are calculated for individual characters. The method is based on eigenimages calculated from edge filtered character images, which enables compact feature vectors that can be computed rapidly. In this study the database contains 2763 different fonts for the English alphabet. To resemble a real life situation, the proposed method is evaluated with printed and scanned text lines and character images. Our evaluation shows that for 99.1% of the queries, the correct font name can be found within the five best matches

Adobe Acrobat image compression: An Investigation into the effects of compression in Acrobat 4.0 on image reproducibility for digital printing

The aim of this project is to investigate each [digitally printed file] compression setting in Acrobat 4.0 Distiller by using and comparing the effects of each on a digitally printed file. The goal is to define the visual results these compressions will yield according to the components of a given file

A new adaptive edge enhancement algorithm for color laser printers

This thesis presents a novel algorithm for improving quality of edges in printed text. The algorithm is designed to add pixels at selected edge locations after halftoning. The extent of the correction is proportional to the “strength” of the edge, as determined by comparing the local differences in a four-pixel neighborhood to a dynamically generated threshold. The process is computationally efficient and requires minimal memory resources. The performance of our proposed algorithm is clearly demonstrated on several characters and lines. While the algorithm aims to improve the quality of printed text (edges), it is possible to extend its application to improvement of any edge identifiable in an image document

Notes on Scholarly Journal Typesetting using Adobe InDesign

As requested, I have put down some notes on the methods and rules for typesetting and page composition of scholarly journals, articles, and books. These are intended to aid in the production of professional-looking digital documents of an academic nature. Adobe InDesign is a desktop publishing software, often marketed as part of Adobe Creative Suite. This is a short, 6-page do\u27s and don\u27ts on achieving a classical scholarly page design. Library publishers may wish to consult and agree or disagree with the thoughts expressed. Three sample templates are attached that may be used and adapted by those who wish

Raster to vector conversion: creating an unique handprint each time

When a person composes a document by hand, there is random variability in what is produced. That is, every letter is different from all others. If the person produces seven a s, none will be the same. This is not true when a computer prints something. When the computer produces seven a s they are all exactly the same. However, even with the variability inherent in a person s handwriting, when two people write something and they are compared side by side, they often appear as different as fonts from two computer families. In fact, if the two were intermixed to produce some text that has characters from each hand, it would not look right! The goal of this application is to improve the ability to digitally create testing materials (i. e., data collection documents) that give the appearance of being filled out manually (that is, by a person). We developed a set of capabilities that allow us to generate digital test decks using a raster database of handprinted characters, organized into hands (a single person s handprint). We wish to expand these capabilities using vector characters. The raster database has much utility to produce digital test deck materials. Vector characters, it is hoped, will allow greater control to morph the digital test data, within certain constraints. The long-term goal is to have a valid set of computer-generated hands that is virtually indistinguishable from characters created by a person