This paper presents a segmentation-based handwriting recognizer and the performance that it achieves on the numerical fields extracted from a large single-writer historical collection. Our recognizer has the particularity that it uses morphing during training: random elastic deformations are applied to fabricate synthetic training character patterns yielding an improved final recognition performance. Two different digit recognizers are evaluated, a multilayer perceptron (MLP) and radial basis function network (RBF), by plugging them into the same left-to-right Viterbi search framework with a tree organization of there cognition lexicon. We also compare with the performance obtained when no dictionary is used to constrain the recognition results.

The performance of off-line text-independent writer verification and identification increases when the documents contain more text. This relation was examined by repeatedly conducting writer verification and identification performance tests while gradually increasing the amount of text on the pages. The experiment was performed on the datasets Firemaker and IAM using four different features. It was also determined what the influence of an unequal amount of text in the documents is. For the best features, it appears that the minimum amount of needed text is about 100 characters.

In this paper, a new method for off-line writer verification and identification is proposed which encodes writer features as a mix of typical handwriting styles, written by so-called vantage writers. Since their handwriting can be shown to the user, the method provides a degree of transparency that is usually not present in automatic verification and identification systems. It acts as a dimensionality reduction of a precomputed basic feature vector. In this experiment, the hinge feature was used. The method was tested with unconstrained connected cursive text in two datasets: a known dataset of 252 writers and 1074 writers from a new, forensic dataset.

In this paper, we describe the structure and the performance of a layout analysis system developed for processing the handwritten documents contained in a large historical collection of very high importance in the Netherlands. We introduce a method based on contour tracing that generates curvilinear separation paths between text lines in order to preserve the ascenders and descenders. Our methods are relevant to research on digitization and retrieval of handwritten historical documents.

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).

The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates.

An effective method for writer identification and verification is based on assuming that each writer acts as a stochastic generator of ink-trace fragments, or graphemes. The probability distribution of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common codebook of graphemes obtained by clustering. In previous studies we used contours to encode the graphemes, in the current paper we explore a complementary shape representation using normalized bitmaps. The most important aim of the current work is to compare three different clustering methods for generating the grapheme codebook: k-means Kohonen SOM 1D and 2D. Large scale computational experiments show that the proposed method is robust to the underlying shape representation used (whether contours or normalized bitmaps), to the size of codebook used (stable performance for sizes from 10/sup 2/ to 2.5 /spl times/ 10/sup 3/) and to the clustering method used to generate the codebook (essentially the same performance was obtained for all three clustering methods).

Automatic text recognition from natural images receives a growing attention because of potential applications in image retrieval, robotics and intelligent transport system. Camera-based document analysis becomes a real possibility with the increasing resolution and availability of digital cameras. Our research objective is a system that reads the text encountered in natural scenes with the aim to provide assistance to visually impaired persons. In the case of a blind person, finding the text region is the first important problem that must be addressed, because it cannot be assumed that the acquired image contains only characters. In a previous paper (N. Ezaki et al., 2004), we propose four text-detection methods based on connected components. Finding small characters needed significant improvement. This paper describes a new text-detection method geared for small text characters. This method uses Fisher's discriminant rate (FDR) to decide whether an image area should be binarized using local or global thresholds. Fusing the new method with a previous morphology-based one yields improved results. Using a controllable Webcam and a laptop PC, we developed a prototype that works in real time. At first, our system tries to find in the image areas with small characters. Then it zooms into the found areas to retake higher resolution images necessary for character recognition. Going from this proof-of-concept to a complete system requires further research effort.

In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO3s) in uppercase 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 uppercase 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 uppercase 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 edge-based orientation and curvature PDF yielded very high correct identification rates.

This paper evaluates the performance of edge-based directional probability distributions as features in writer identification in comparison to a number of non-angular features. It is noted that the joint probability distribution of the angle combination of two "hinged" edge fragments outperforms all other individual features. Combining features may improve the performance. Limitations of the method pertain to the amount of handwritten material needed in order to obtain reliable distribution estimates. The global features treated in this study are sensitive to major style variation (upper- vs lower case), slant, and forged styles, which necessitates the use of other features in realistic forensic writer identification procedures.

This paper evaluates the performance of edge-based directional probability distributions as features in writer identification in comparison to a number of nonangular features. It is noted that angular features outperform all other features. However, the nonangular features provide additional valuable information. Rank-combination was used to realize a sparse-parametric combination scheme based on nearest-neighbor search. Limitations of the proposed methods pertain to the amount of handwritten material needed in order to obtain reliable distribution estimates. The global features treated in this study are sensitive to major style variation (upper- vs lower case), slant, and forged styles, which necessitates the use of other features in realistic forensic writer identification procedures.