Abstract There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author’s identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.
Grapheme-based features extract allographic patterns and map them into a common space (also known as a codebook). Connected-component contours (CO @math ) are proposed in @cite_14 @cite_55 for writer identification blue using upper-case Western scripts, and blue have more recently been extended to Fraglets @cite_36 @cite_11 blue for cursive handwriting documents. Small patches extracted from characters are used as graphemes in @cite_52 @cite_5 and synthetic graphemes blue which have been generated based on the beta-elliptic model are used in @cite_28 on Arabic handwritten document images. The junctions blue in handwritten images are very useful for measuring the handwriting style and they are considered blue to be basic elements of the handwritten text for writer identification in @cite_7 . SIFT feature @cite_17 and RootSIFT descriptor are also used for writer identification @cite_39 @cite_44 . Both the textural-based and grapheme-based features can be used to generate more powerful features by the co-occurrence or joint feature principle, which can be found in @cite_13 . Table shows blue the advantages and disadvantages of each method mentioned above.
@cite_14 @cite_7 @cite_36 @cite_28 @cite_55 @cite_52 @cite_17 @cite_39 @cite_44 @cite_5 @cite_13 @cite_11
2090985198 2152446828 2018503688 2170737087
Rapid movement generation models are described in the literature as an efficient tool to apprehend the handwriting behavior. Fields of application are diverse, including handwriting description, regeneration, and more recently OCR. In this paper, we propose a grapheme-based approach to offline Arabic writer identification and verification. Rather than extracting naturel graphemes from a training corpus using segmentation and clustering, it synthesizes its own graphemes based on the beta-elliptic model. Originality lies in the independence of the grapheme codebook from any training process, and the use of a model instead. One full and four partial codebooks are generated and tested. Using feature selection, raw codebooks are reduced in size with respect to FDR, FDR and cross-correlation, and random subsampling criteria. A total of 60 feature vectors are extracted using template matching, and evaluated with 411 individual writers from the IFN ENIT database. The results presented in this study demonstrated the wide representativity and the good generalization capability of synthetic codebooks. We obtained a top1 rate=90.02 and a top5 rate=96.35 for writer identification, and an EER=2.1 for writer verification. Our approach showed better properties than most of the surveyed techniques in terms of supported corpus size and identification rates. To the best of our knowledge, this study is among the first to exploit the concept of model-based synthetic codebooks in writer identification and verification. HighlightsUniversal synthetic codebooks are used for writer identification and verification.The beta-elliptic handwriting generation model is used to create the codebooks.Neither corpus-driven training nor prior segmentation is needed in this approach.Synthetic codebook demonstrated good generalization capabilities for Arabic. 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 CC's 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. In this paper, a new model-based writer identification scheme using histogram symbolic representation approach is proposed. In the proposed scheme, initially, some pre-processing techniques are employed to enhance image quality and extract text-lines from each handwritten document image. For each extracted text-line, a set of 92 features are computed based on analysis of connected component, enclosed region, lower and upper contours, fractal code, and Curve let. Considering the extracted feature vectors, a histogram is created for each feature of every writer as a histogram-valued symbolic data. This process results in a handwriting style model for each individual that consists of a set of histograms. To evaluate the proposed scheme, two different handwritten datasets written in two different scripts (Kannada as an Indian based script and English) were used. The first dataset contains 228 pages written in Kannada by 57 people. The other one is the dataset used in SigWiComp2013 composed of 330 document pages written in English by 55 individuals. The same criteria used in the SigWiComp2013 were followed in our evaluation strategy. Concerning the Kannada dataset, an F-measure of 92.79 was obtained when 114 documents were used in learning stage and the rest (114) were used for testing. For the SigWiComp2013 dataset an F-measure of 26.67 was obtained that is fairly comparable to the best result reported in the literature. 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