In this article, methods for user recognition by online handwriting are experimentally analyzed using a combination of demographic data of users in relation to their handwriting habits. Online handwriting as a biometric method is characterized by having high variations of characteristics that influences the reliance and security of this method. These variations have not been researched in detail so far. Especially in cross-cultural application it is urgent to reveal the impact of personal background to security aspects in biometrics. Metadata represent the background of writers, by introducing cultural, biological and conditional (changing) aspects like fist language, country of origin, gender, handedness, experiences the influence handwriting and language skills. The goal is the revelation of intercultural impacts on handwriting in order to achieve higher security in biometrical systems. In our experiments, in order to achieve a relatively high coverage, 48 different handwriting tasks have been accomplished by 47 users from three countries (Germany, India and Italy) have been investigated with respect to the relations of metadata and biometric recognition performance. For this purpose, hypotheses have been formulated and have been evaluated using the measurement of well-known recognition error rates from biometrics. The evaluation addressed both: system reliance and security threads by skilled forgeries. For the later purpose, a novel forgery type is introduced, which applies the personal metadata to security aspects and includes new methods of security tests. Finally in our paper, we formulate recommendations for specific user groups and handwriting samples.

The automatic extraction of handwriting styles is an important process that can be used for various applications in the processing of handwriting. We propose a novel method that employs hierarchical clustering to explore prominent clusters of handwriting. So-called membership vectors are introduced to describe the handwriting of a writer. Each membership vector reveals the frequency of occurrence of prototypical characters in a writer’s handwriting. By clustering these vectors, consistent handwriting styles can be extracted, similar to the exemplar handwritings documented in copybooks. The results presented here are challenging. The most prominent handwriting styles detected correspond to the broad style categories cursive, mixed, and print. 1.

We address the problem of content-based image retrieval in the context of complex document images. Complex document are documents that typically start out on paper and are then electronically scanned. These documents have rich internal structure and might only be available in image form. Additionally, they may have been produced by a combination of printing technologies (or by handwriting); and include diagrams, graphics, tables and other non-textual elements. Large collections of such complex documents are commonly found in legal and security investigations. The indexing and analysis of large document collections is currently limited to textual features based OCR data and ignore the structural context of the document as well as important non-textual elements such as signatures, logos, stamps, tables, diagrams, and images. Handwritten comments are also normally ignored due to the inherent complexity of offline handwriting recognition. We address important research issues concerning content-based document image retrieval and describe a prototype for integrated retrieval and aggregation of diverse information contained in scanned paper documents we are developing. Such complex document information processing combines several forms of image processing together with textual/linguistic processing to enable effective analysis of complex document collections, a necessity for a wide range of applications. Our prototype automatically generates rich metadata about a complex document and then applies query tools to integrate

Abstract: Paper documents are routinely found in general litigation and criminal and terrorist investigations. The current state-of-the-art processing of these documents is to simply OCR them and search strictly the text. This ignores all handwriting, signatures, logos, images, watermarks, and any other non-text artifacts in a document. Technology, however, exists to extract key metadata from paper documents such as logos and signatures and match these against a set of known logos and signatures. We describe a prototype that moves beyond simply the OCR processing of paper documents and relies on additional documents artifacts rather than only on text in the search process. We also describe a benchmark developed for the evaluation of paper document search systems.

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We describe our work on text-image alignment in context of building a historical document retrieval system. We aim at aligning images of words in handwritten lines with their text transcriptions. The images of handwritten lines are automatically segmented from the scanned pages of historical documents and then manually transcribed. To train automatic routines to detect words in an image of handwritten text, we need a training set- images of words with their transcriptions. We present our results on aligning words from the images of handwritten lines and their corresponding text transcriptions. Alignment based on the longest spaces between portions of handwriting is a baseline. We then show that relative lengths, i.e. proportions of words in their lines, can be used to improve the alignment results considerably. To take into account the relative word length, we define the expressions for the cost function that has to be minimized for aligning text words with their images. We apply right to left alignment as well as alignment based on exhaustive search. The quality assessment of these alignments shows correct results for 69 % of words from 100 lines, or 90 % of partially correct and correct alignments combined.

The paper attempts to create a handwritten document retrieval system suitable for Tamil language, with a view to record traditional literature content for future reference. It projects a search mechanism to access the query word images using a statistical model based methodology. The scheme revolves around a well defined procedure which results in word models from where the search word can be recognised and the relevant documents retrieved. The approach involves the use of hidden Markov models (HMM) to characterize the features of the dynamically varying strokes of handwritten characters. The strategy is investigated for a sample document set over a commonly used literature. The results reveal that the system yields a reasonable performance with considerable accuracy. The highlight of this procedure is that it can effectively segment differently written words from text lines in a document and imbibes in it a flexibility to cover a wide range of tilts in the strokes that are attached to the different words.