In this paper, we evaluate the performance on Arabic handwriting of the text-independent writer identification methods that we developed and tested on Western script in recent years. We use the IFN/ENIT data in the experiments reported here and our tests involve 350 writers. The results show that our methods are very effective and the conclusions drawn in previous studies remain valid also on Arabic script. High performance is achieved by combining textural features (joint directional probability distributions) with allographic features (grapheme-emission distributions). 1.

In this paper, a new technique for off-line writer identification is presented, using connected-component contours (COCOCOs or CO³s) in upper-case handwritten samples. In our model, the writer is considered to be characterized by a stochastic pattern generator, producing a family of connected components for the upper-case character set. Using a codebook of CO³s from an independent training set of 100 writers, the probability-density function (PDF) of CO³s was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO³ PDF for identifying individual writers on the basis of a single sentence of upper-case characters. The proposed automatic approach bridges the gap between image-statistics approaches on one end and manually measured allograph features of individual characters on the other end. Combining the CO³ PDF with an independent edgebased orientation and curvature PDF yielded very high correct identification rates.

important role in forensic document examination. However, so far there lacks a comprehensive and quantitative study on individuality of handwritten characters. Based on a large number of handwritten characters extracted from handwriting samples of 1000 individuals in US, the individuality of handwritten characters has been quantitatively measured through identification and verification models. Our study shows that in general alphabetic characters bear more individuality than numerals and use of a certain number of characters will significantly outperform the global features of handwriting samples in handwriting identification and verification. Moreover, the quantitative measurement of discriminative powers of characters offers a general guidance for selecting most-informative characters in examining forensic documents.

In this paper, a method for off-line writer identification is presented, using the contours of fragmented connected-components in mixed-style handwritten samples of limited size. The writer is considered to be characterized by a stochastic pattern generator, producing a family of character fragments (fraglets). Using a codebook of such fraglets from an independent training set, the probability distribution of fraglet contours was computed for an independent test set. Results revealed a high sensitivity of the fraglet histogram in identifying individual writers on the basis of a paragraph of text. Large-scale experiments on the optimal size of Kohonen maps of fraglet contours were performed, showing usable classification rates within a non-critical range of Kohonen map dimensions. Further validation experiments on variable-sized random subsets from an independent set of 215 writers gives additional support for the proposed method. The proposed automatic approach bridges the gap between imagestatistics approaches and manual character-based methods.

The design and functionality of a versatile search engine on handwritten documents is described. Documents are indexed using global image features, e.g., stroke width, slant, word gaps, as well local features that describe shapes of characters and words. Image indexing is done automatically using page analysis, page segmentation, line separation, word segmentation and recognition of characters and words. Several types of searches are

Learning strategies and classification methods for verification of signatures from scanned documents are proposed and evaluated. Learning strategies considered are writerindependent– those that learn from a set of signature samples(including forgeries) prior to enrollment of a writer, and writer dependent – those that learn only from a newly enrolled individual. Classification methods considered include two distance based methods (one based on a threshold, which is the standard method of signature verification and biometrics, and the other based on a distance probability distribution), a Nave Bayes (NB) classifier based on pairs of feature bit values and a support vector machine (SVM). Two scenarios are considered for the writerdependent scenario: (i) without forgeries (one-class problem) and (ii) with forgery samples being available (twoclass problem). The features used to characterize a signature capture local geometry, stroke and topology information in the form of a binary vector. In the one-class scenario distance methods are superior while in the two-class SVM based method outperforms the other methods. 1.

In the analysis and recognition of handwriting, a useful first task is to assign ground truth for words in the writing. Such an assignment is useful for various subsequent machine learning tasks for performing automatic recognition, writer verification, etc. Since automatic word segmentation and recognition can be error prone, an intermediate approach is to use a text file that is a transcription of the handwriting image for performing ground truth assignment. This paper describes an algorithm for finding the best word level alignment between the transcript and the handwriting image. The algorithm is useful in tasks such as: (i) extracting words and characters as characteristic elements in writer verification and identification tasks; (ii) creating a large ground-truthed dataset for handwriting document analysis (in word and even character levels); (iii) indexing a collection of handwritten materials for document retrieval, such as for historical manuscripts. The algorithm achieves an 84.7 % accuracy in aligning words on whole images when evaluated on 20 pages from a handwriting database created for forensic document examination studies.

Using handwritten characters we address two questions (i) what is the group identification performance of different alphabets (upper and lower case) and (ii) what are the best characters for the verification task (same writer/different writer discrimination) knowing demographic information about the writer such as ethnicity, age or sex. The Bhattacharya distance is used to rank different characters by their group discriminatory power and the k-nn classifier to measure the individual performance of characters for group identification. Keywords: Handwriting Identification,Character Discriminability 1.

In searching a repository of business documents, a task of interest is that of using a query signature image to retrieve from a database, other signatures matching the query. The signature retrieval task involves a two-step process of extracting all the signatures from the documents and then performing a match on these signatures. This paper presents a novel signature retrieval strategy, which includes a technique for noise and printed text removal from signature images, previously extracted from business documents. Signature matching is based on a normalized correlation similarity measure using global shape-based binary feature vectors. In a retrieval task involving a database of 447 signatures, on an average 4.43 out of the top 5 choices were signatures belonging to the writer of the queried signature. On considering the Top 10 ranks, a F-measure value of 76.3 was obtained and the precision and recall values at this Fmeasure were 74.5 % and 78.28 % respectively. 1.

A statistical model for determining whether a pair of documents, a known and a questioned, were written by the same individual is proposed. The model has the following four components: (i) discriminating elements, e.g., global features and characters, are extracted from each document, (ii) differences between corresponding elements from each document are computed, (iii) using conditional probability estimates of each difference, the log-likelihood ratio (LLR) is computed for the hypotheses that the documents were written by the same or different writers; the conditional probability estimates themselves are determined from labelled samples using either Gaussian or gamma estimates for the differences assuming their statistical independence, and (iv) distributions of the LLRs for same and different writer LLRs are analyzed to calibrate the strength of evidence into a standard nine-point scale used by questioned document examiners. The model is illustrated with experimental results for a specific set of discriminating elements. 1.