Marius Bulacu

Text-Independent Writer Identification and Verification Using Textural and Allographic Features

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

Text detection from natural scene images: towards a system for visually impaired persons

We propose a system that reads the text encountered in natural scenes with the aim to provide assistance to the visually impaired persons. This paper describes the system design and evaluates several character extraction 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. However, 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. 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. In the present paper, we propose four character-extraction methods based on connected components. We tested the effectiveness of our methods on the ICDAR 2003 Robust Reading Competition data. The performance of the different methods depends on character size. In the data, bigger characters are more prevalent and the most effective extraction method proves to be the sequence: Sobel edge detection, Otsu binarization, connected component extraction and rule-based connected component filtering.

Automatic writer identification using connected-component contours and edge-based features of uppercase Western script

In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO/sup 3/s) 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 CO/sup 3/s from an independent training set of 100 writers, the probability-density function (PDF) of CCs was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO/sup 3/ 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 CO/sup 3/ PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.

Writer identification using directional ink-trace width measurements

As suggested by modern paleography, the width of ink traces is a powerful source of information for off-line writer identification, particularly if combined with its direction. Such measurements can be computed using simple, fast and accurate methods based on pixel contours, the combination of which forms a powerful feature for writer identification: the Quill feature. It is a probability distribution of the relation between the ink direction and the ink width. It was tested in writer identification experiments on two datasets of challenging medieval handwriting and two datasets of modern handwriting. The feature achieved a nearest-neighbor accuracy in the range of 63-95%, which even approaches the performance of two state-of-the-art features in contemporary-writer identification (Hinge and Fraglets). The feature is intuitive and explainable and its principle is supported by a model of trace production by a quill. It illustrates that ink width patterns are valuable. A slightly more complex variant of Quill, QuillHinge, scored 70-97% writer identification accuracy. The features are already being used by domain experts using a graphical interface.

Automatic writer identification using fragmented connected-component contours

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 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 image statistics approaches and manual character-based methods.

Layout Analysis of Handwritten Historical Documents for Searching the Archive of the Cabinet of the Dutch Queen

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.

Improved text-detection methods for a camera-based text reading system for blind persons

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 Fishers 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.

Writer Style from Oriented Edge Fragments

In this paper we evaluate the performance of edge-based directional probability distributions extracted from handwriting images as features in forensic writer identification in comparison to a number of non-angular features. We compare the performances of the features on lowercase and uppercase handwriting. In an effort to gain location-specific information, new versions of the features are computed separately on the top and bottom halves of text lines and then fused. The new features deliver significant improvements in performance. We report also on the results obtained by combining features using a voting scheme.

A comparison of clustering methods for writer identification and verification

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).

Sparse-parametric writer identification using heterogeneous feature groups

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.