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.
Several powerful approaches have recently been proposed for writer identification, which rely on local descriptors that capture the texture, shape and curvature properties of the handwriting. In this paper we use combinations of three of these features (K-Adjacent Segments, SURF, and Contour Gradient Descriptors), to address the writer identification problem. Experiments demonstrate that feature combinations outperform individual features, resulting in state-of-the-art performance on three datasets. This paper presents a text-independent writer recognition method in a multi-script environment. Handwritten texts in Greek and English are considered in this study. The objective is to recognize the writer of a handwritten text in one script from the samples of the same writer in another script and hence validate the hypothesis that writing style of an individual remains constant across different scripts. Another interesting aspect of our study is the use of short handwritten texts which was implied to resemble the real life scenarios where the forensic experts, in general, find only short pieces of texts to identify a given writer. The proposed method is based on a set of run-length features which are compared with the well-known state-of-the-art features. Classification is carried out using K-Nearest Neighbors (K-NN) and Support Vector Machines (SVM). The experimental results obtained on a database of 126 writers with 4 samples per writer show that the proposed scheme achieves interesting performances on writer identification and verification in a multi-script environment. 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. Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set and a simple end-to-end pipeline demonstrate State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple 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 Highlights? A segmentation free process for writer identification verification. ? Evaluation of two texture descriptors (LBP and LPQ) for writer identification verification. ? Evaluation of the dissimilarity-based approach for writer identification. ? Discussion about the number and size of the references for the dissimilarity-based approach. In this work, we discuss the use of texture descriptors to perform writer verification and identification. We use a classification scheme based on dissimilarity representation, which has been successfully applied to verification problems. Besides assessing two texture descriptors (local binary patterns and local phase quantization), we also address important issues related to the dissimilarity representation, such as the impact of the number of references used for verification and identification, how the framework performs on the problem of writer identification, and how the dissimilarity-based approach compares to other feature-based strategies. In order to meet these objectives, we carry out experiments on two different datasets, the Brazilian forensic letters database and the IAM database. Through a series of comprehensive experiments, we show that both LBP- and LPQ-based classifiers are able to surpass previous results reported in the literature for the verification problem by about 5 percentage points. For the identification problem, the proposed approach using LPQ features is able to achieve accuracies of 96.7 and 99.2 on the BFL and IAM and databases respectively. We describe how oriented Basic Image Feature Columns (oBIF Columns) can be used for writer identification and how this texture-based scheme can be enhanced by encoding a writer's style as the deviation from the mean encoding for a population of writers. We hypothesise that this deviation, the Delta encoding, provides a more informative encoding than the texture-based encoding alone. The methods have been evaluated using the IAM dataset and by making entries to two top international competitions for assessing the state-of-the-art in writer identification. We demonstrate that the oBIF Column scheme on its own is sufficient to gain a performance level of 99 when tested using 300 writers from the IAM dataset. However, on the more challenging competition datasets, significantly improved performance was obtained using the Delta encoding scheme, which achieved first place in both competitions. In our characterisation of the Delta encoding, we demonstrate that the method is making use of information contained in the correlation between the written style of different textual elements, which may not be used by other methods. HighlightsWe present a full description of the oBIF Column method as applied to writer identification.We evaluate the oBIF Column encoding scheme using the IAM dataset, producing a performance of 99 for 301 writers.We present the Delta encoding, an addition to the oBIF Column scheme.We evaluate the Delta encoding using two leading international competitions, gaining first place in both.We show how our method uses different sources of information. The style of people's handwriting is a biometric feature that is used in person authentication. In this paper, we have proposed a text independent method for Persian writer identification. In the proposed method, pattern based features are extracted from data using Gabor and XGabor filter. The extracted features are represented for each person by using a graph that is called FRG (feature relation graph). This graph is constructed using relations between extracted features by employing a fuzzy method. The fuzzy method determines the similarity between features extracted from different handwritten instances of each person. In the identification phase, a graph similarity approach is employed to determine the similarity of the FRG generated from the test data and the FRGs generated by training data. The experimental results were satisfactory and the proposed method got about 100 accuracy on a dataset with 100 writers when enough training data was used. However, this method has been applied on Persian handwritings but we believe it can be extended on other languages especially in data representation and classification parts. We propose an effective method for automatic writer recognition from unconstrained handwritten text images. Our method relies on two different aspects of writing: the presence of redundant patterns in the writing and its visual attributes. Analyzing small writing fragments, we seek to extract the patterns that an individual employs frequently as he writes. We also exploit two important visual attributes of writing, orientation and curvature, by computing a set of features from writing samples at different levels of observation. Finally we combine the two facets of handwriting to characterize the writer of a handwritten sample. The proposed methodology evaluated on two different data sets exhibits promising results on writer identification and verification. 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. This paper presents a method for extracting rotation-invariant features from images of handwriting samples that can be used to perform writer identification. The proposed features are based on the Hinge feature [1], but incorporating the derivative between several points along the ink contours. Finally, we concatenate the proposed features into one feature vector to characterize the writing styles of the given handwritten text. The proposed method has been evaluated using Fire maker and IAM datasets in writer identification, showing promising performance gains. Many techniques have been reported for handwriting-based writer identification. Most techniques assume that the written text is fixed (e.g., in signature verification). In this paper we attempt to eliminate this assumption by presenting a novel algorithm for automatic text-independent writer identification. Given that the handwriting of different people can often be visually distinctive, we take a global approach based on texture analysis, where each writer's handwriting is regarded as a different texture. In principle this allows us to apply any standard texture recognition algorithm for the task (e.g., the multichannel Gabor filtering technique). Results of 95.0 accuracy on the classification of 300 test documents front 20 writers are very promising. The method is shown to be robust to noise and contents. This paper presents a method for performing offline writer identification by using K-adjacent segment (KAS) features in a bag-of-features framework to model a user's handwriting. This approach achieves a top 1 recognition rate of 93 on the benchmark IAM English handwriting dataset, which outperforms current state of the art features. Results further demonstrate that identification performance improves as the number of training samples increase, and additionally, that the performance of the KAS features extend to Arabic handwriting found in the MADCAT dataset. Feature engineering takes a very important role in writer identification which has been widely studied in the literature. Previous works have shown that the joint feature distribution of two properties can improve the performance. The joint feature distribution makes feature relationships explicit instead of roping that a trained classifier picks up a non-linear relation present in the data. In this paper, we propose two novel and curvature-free features: run-lengths of local binary pattern (LBPruns) and cloud of line distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized and gray scale images. The COLD feature is the joint distribution of the relation between orientation and length of line segments obtained from writing contours in handwritten documents. Our proposed LBPruns and COLD are textural-based curvature-free features and capture the line information of handwritten texts instead of the curvature information. The combination of the LBPruns and COLD features provides a significant improvement on the CERUG data set, handwritten documents on which contain a large number of irregular-curvature strokes. The results of proposed features evaluated on other two widely used data sets (Firemaker and IAM) demonstrate promising results. In this paper, we propose two novel textural-based features for writer identification: CoHinge and QuadHinge which are based on the spatial and attribute co-occurrence of the Hinge kernel. The CoHinge feature is the joint distribution of the Hinge kernel on two different pixels of writing contours and the QuadHinge feature is the joint distribution of angles and curvature information of contour fragments. We evaluate the proposed features on five benchmark data sets and their combined large set and the experimental results demonstrate the discriminative and powerful of the proposed features. In this paper, an off-line, text independent system for writer identification and verification of handwritten text lines using Hidden Markov Model (HMM) based recognizers is presented. For each writer, an individual recognizer is built and trained on text lines of that writer. This results in a number of recognizers, each of which is an expert on the handwriting of exactly one writer. In the identification and verification phase, a text line of unknown origin is presented to each of these recognizers and each one returns a transcription that includes the log-likelihood score for the generated output. These scores are sorted and the resulting ranking is used for both identification and verification. Several confidence measures are defined on this ranking. The proposed writer identification and verification system is evaluated using different experimental setups. Writer identification is the task of determining the author of a sample handwriting from a set of writers. In this paper, we propose Gaussian Mixture Models (GMMs) to address the task of off-line, text independent writer identification of text lines. The resulting system is compared to a system that uses a Hidden Markov Model (HMM) based approach. While the GMM based system is conceptually much simpler and faster to train than the HMM based system, it achieves a significantly higher writer identification rate of 98.46 on a data set of 4,103 text lines coming from 100 writers.