Writer Identification Research Articles

Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens

Handwriting is one of the most frequently occurring patterns in everyday life and with it comes challenging applications such as handwriting recognition, writer identification and signature verification. In contrast to offline HWR that only uses spatial information (i.e., images), online HWR uses richer spatio-temporal information (i.e., trajectory data or inertial data). While there exist many offline HWR datasets, there are only little data available for the development of OnHWR methods on paper as it requires hardware-integrated pens. This paper presents data and benchmark models for real-time sequence-to-sequence learning and single character-based recognition. Our data are recorded by a sensor-enhanced ballpoint pen, yielding sensor data streams from triaxial accelerometers, a gyroscope, a magnetometer and a force sensor at 100 Hz. We propose a variety of datasets including equations and words for both the writer-dependent and writer-independent tasks. Our datasets allow a comparison between classical OnHWR on tablets and on paper with sensor-enhanced pens. We provide an evaluation benchmark for seq2seq and single character-based HWR using recurrent and temporal convolutional networks and transformers combined with a connectionist temporal classification (CTC) loss and cross-entropy (CE) losses. Our convolutional network combined with BiLSTMs outperforms transformer-based architectures, is on par with InceptionTime for sequence-based classification tasks and yields better results compared to 28 state-of-the-art techniques. Time-series augmentation methods improve the sequence-based task, and we show that CE variants can improve the single classification task. Our implementations together with the large benchmark of state-of-the-art techniques of novel OnHWR datasets serve as a baseline for future research in the area of OnHWR on paper.

Offline writer identification using a developed deep neural network based on a novel signature dataset

Offline writer identification using a developed deep neural network based on a novel signature dataset

A generalized line segmentation method for multi-script handwritten text documents

Handwritten document image segmentation into text-lines is a crucial stage towards unconstrained handwritten document recognition. In the context of Indian subcontinent various scripts are used for communication where a system for multi-script handwritten text line segmentation is very much essential. This paper presents a multi-script text line segmentation algorithm based on newly developed light projection, start point detection, and boundary tracking methods. The proposed approach is capable of overcoming most of the hindrance faced by state-of-the-art methods. The experiment is performed on our proposed Bangla handwritten document image dataset WBSUBNdb_text and also on a variety of well-known public handwritten datasets namely: CMATERdb, PhDIndic_11, KHATT, HIT-MW, ISI Bengali Writer Identification/Verification dataset, ICDAR 2013 segmentation contest dataset, ICDAR 2013 writer identification contest benchmark dataset, and obtained promising results.

Text line segmentation from struck-out handwritten document images

In the case of freestyle everyday handwritten documents, writing, erasing, striking out, and overwriting are common behaviors of the writers. This not cleanly-written text poses significant challenges for text line segmentation. Accurate text line segmentation in handwritten documents is essential to the success of several real-world applications, such as answer script evaluation, fraud document identification, writer identification, document age estimation and writer gender classification, to name a few. This paper proposes the first, to the authors’ best knowledge, text line segmentation approach that is applicable in the presence of both cleanly-written and struck-out text. The approach consists of three steps. In the first step, components - at the word level - are detected in the input handwritten document images (containing both cleanly-written and struck-out text) based on stroke width information estimation, filtering of noise, and morphological operations. In the second step, the struck-out components are identified using the DenseNet deep learning model and treated differently to clean text in further analysis. In the third step, geometrical spatial features, the direction between candidate components and the overall text line, and the common overlapping region between adjacent components are evaluated to progressively form text lines. To evaluate the proposed steps and compare the proposed method to the state-of-the-art, experiments have been conducted on a new problem-focused dataset containing instances of struck-out text in handwritten documents, as well as on two standard datasets (ICDAR2013 text line segmentation contest dataset and ICDAR2019 HDRC dataset) to show the proposed steps are effective and useful, with superior performance compared to existing methods.

A texture-based approach for offline writer identification

Offline handwriting has become a very important part in the field of behavioral biometrics. In this paper, we proposed an approach to identify writers from their handwritten documents. The foremost contribution of this study is to suggest the use of co-occurrence features to improve the performance of writer identification. A contour texture-based feature is extracted from preprocessed regions of interest (components or sub-images) and their exterior contours are based primarily on Modified Local Binary Pattern (MLBP) and Ink-trace Width and Shape Letters (IWSL) measurements. Considering the contour as a texture, and using these textural descriptors, the joint probability distributions of MLBP and IWSL on different pixels are calculated in order to determine the similarities between different images of handwriting. Identification is carried out using the nearest neighbor rule and Chi-square distance. The proposed system has been evaluated on eight well-known handwriting databases (Arabic IFN/ENIT and KHATT, English IAM and CVL, Dutch Firemaker, Portuguese BFL, Chinese CERUG-CN, and English/Greek ICDAR2013). Experimental results show that the recommended scheme achieves the highest performance on KHATT, CVL, Firemaker, BFL, CERUG-CN, and ICDAR2013 databases, and that it demonstrates competitive performance on IFN/ENIT and IAM databases as compared to those reported by the state-of-the-art identification systems.

New Deep Spatio-Structural Features of Handwritten Text Lines for Document Age Classification

Document age estimation using handwritten text line images is useful for several pattern recognition and artificial intelligence applications such as forged signature verification, writer identification, gender identification, personality traits identification, and fraudulent document identification. This paper presents a novel method for document age classification at the text line level. For segmenting text lines from handwritten document images, the wavelet decomposition is used in a novel way. We explore multiple levels of wavelet decomposition, which introduce blur as the number of levels increases for detecting word components. The detected components are then used for a direction guided-driven growing approach with linearity, and nonlinearity criteria for segmenting text lines. For classification of text line images of different ages, inspired by the observation that, as the age of a document increases, the quality of its image degrades, the proposed method extracts the structural, contrast, and spatial features to study degradations at different wavelet decomposition levels. The specific advantages of DenseNet, namely, strong feature propagation, mitigation of the vanishing gradient problem, reuse of features, and the reduction of the number of parameters motivated us to use DenseNet121 along with a Multi-layer Perceptron (MLP) for the classification of text lines of different ages by feeding features and the original image as input. To demonstrate the efficacy of the proposed model, experiments were conducted on our own as well as standard datasets for both text line segmentation and document age classification. The results show that the proposed method outperforms the existing methods for text line segmentation in terms of precision, recall, F-measure, and document age classification in terms of average classification rate.

Genome-Wide Identification of m6A Writers, Erasers and Readers in Poplar 84K.

N6-methyladenosine (m6A) RNA modification is a conserved mechanism to regulate gene expression that plays vital roles in the development of plants. However, the m6A RNA modification in forest trees remains limited. Here, we performed a complete analysis of m6A writers, erasers and readers in Poplar 84K, including gene location, gene structures, conserved motifs, phylogenetic relationships, promoter analysis, expression profiles and the homology modeling. We have identified 61 m6A pathway genes in Poplar 84K (Populus alba × Populus glandulosa), including 14 m6A writers, 14 m6A erasers and 33 m6A readers. Phylogenetic analysis indicated that the m6A writers and erasers were clustered into four groups and m6A readers were clustered into two groups. Promoter analysis showed that m6A pathway genes were mainly responsive to low oxygen followed by ABA and ethylene. The expression of the identified m6A pathway genes showed tissue-specific expression patterns in leaves, xylem, phloem and roots. Moreover, 17 genes were significantly up-regulated and 13 genes were significantly down-regulated in poplar overexpressing the transcription factor LBD15. Homology modeling and molecular docking results suggested that PagFIP37b was most likely to be regulated by LBD15, and the qPCRshowed that PagFIP37b were up-regulated in the LBD15-oe plants. The results provide insights that aid in the future elucidation of the functions of these m6A pathway genes and the epigenetic regulation mechanism of these genes in Poplar 84K.

Open writer identification from offline handwritten signatures by jointing the one-class symbolic data analysis classifier and feature-dissimilarities

Open writer identification from offline handwritten signatures by jointing the one-class symbolic data analysis classifier and feature-dissimilarities

A graph-based solution for writer identification from handwritten text

A graph-based solution for writer identification from handwritten text

A deep learning based system for writer identification in handwritten Arabic historical manuscripts

A deep learning based system for writer identification in handwritten Arabic historical manuscripts

Text-Dependent and Text-Independent Writer Identification Approaches

Writer identification is a wide-spreading biometric which can be used as a legitimate mean to identify an individual. It facilitates the experts to automatically identify the person in many security concerns applications such as forensic science. Due to this, much attention has been drawn in this field from the last few decades. On the basis of input text, it can have various forms like online, offline, text-dependent or text-independent writer identification. The paper will present a systematic study on text-dependent and text-independent writer identification of handwritten text images for various Indic and non-Indic scripts. The various segmentation techniques used to segment handwritten text are also presented in detail. The various datasets available for researchers are given for various scripts such as English, Arabic, Chinese, Japanese, Dutch, Farsi, Devanagari, Bangla, and Kannada discussed by doing exhaustive analysis of various studies. We hope that our research will be helpful in giving better understanding of the area and provides various directions for further research.

Feature learning and encoding for multi-script writer identification

Feature learning and encoding for multi-script writer identification

RSTC: A New Residual Swin Transformer for Offline Word-Level Writer Identification

Writer identification has steadily progressed in recent decades owing to its widespread application. Scenarios with extensive handwriting data such as page-level or sentence-level have achieved satisfactory accuracy, however, word-level offline writer identification is still challenging owing to the difficulty of learning good feature representations with scant handwriting data. This paper proposes a new Residual Swin Transformer Classifier (RSTC), comprehensively aggregating local and global handwriting styles and yielding robust feature representations with single-word images. Local information is modeled by the Transformer Block through interacting strokes. Global information is featurized by holistic encoding using the Identity Branch and Global Block. Moreover, the pre-training technique is exploited to transfer reusable knowledge learned from a task similar to writer identification, strengthening the model’s representation of handwriting features. The proposed method is tested on the IAM and CVL benchmark datasets and achieves state-of-the-art performance, which demonstrates RSTC’s superior modeling capability on word-level writer identification.

Writer identification using textural features

Writer Identification has gained increasing importance in the scientific community in recent years. In this paper, we propose an approach based on the combination of local textural descriptors and encoding methods (VLAD and Triangulation Embedding). The tests carried out in the bilingual LAMIS dataset made it possible to reach 100% in the Arabic version and 100% in the French version.

Funnelling Ensemble Method for Writer Identification (Fem-Wi)

Funnelling Ensemble Method for Writer Identification (Fem-Wi)

Offline Text-Independent Writer Identification Using a Codebook with Structural Features

Offline Text-Independent Writer Identification Using a Codebook with Structural Features

Off-Line Writer Identification Based on Small Segments of Handwritten Text and Convolutional Neural Networks

Off-Line Writer Identification Based on Small Segments of Handwritten Text and Convolutional Neural Networks

A Survey on Different Feature Extraction Methods for Writer Identification and Verification

A Survey on Different Feature Extraction Methods for Writer Identification and Verification

Writer identification using VLAD Encoding of the Histogram of Gradient Angle Distribution

The use of computers and automatic systems has enabled scientific researchers to improve the classification rate in the field of writer identification. In our paper, we will propose an identification system based on the use of Histogram of Gradient Angle Distribution (HGAD) in square patches centered around Harris Keypoint locations. A global descriptor per image is calculated subsequently via the VLAD encoding of the local descriptors relating to the histograms of the square patches. The study carried out on two public datasets CVL and BFL made it possible to achieve very interesting identification rates with 99.4% in BFL and 99.7% in CVL.

Writer Identification Based on Hand Writing using Artificial Neural Network

Humans are social beings who depend on social interaction. Social interaction that is often used is communication. Communication is one of the bridges to connect social relations between humans. Communication can be delivered in two ways, namely verbal or nonverbal. Handwriting is an example of nonverbal communication using paper and writing utensils. Each individual's writing has its own uniqueness so that handwriting often becomes the character or characteristic of the author. The handwriting pattern usually becomes a character for the writer so that people who recognize the writing will easily guess the ownership of the related handwriting. However, handwriting is often used by irresponsible people in the form of handwriting falsification. The acts of writing falcification often occur in the workplace or even in the field of education. This is one of the driving factors for creating a reliable system in tracking someone's handwriting based on their ownership. In this study, we will discuss the identification of a person's handwriting based on their ownership. The output of this research is in the form of ID from the author and accuracy in the form of percentage of system reliability in identifying. The results of this study are expected to have a good impact on all parties, in order to minimize plagiarism. Identification of handwriting to be built consists of two main processes, namely the training phase and the testing phase. At the training stage, the handwritten image is subjected to several processes, namely threshold, wavelet conversion, and then will be trained using the Backpropagation Artificial Neural Network. In the testing phase, the process is the same as in the training phase, but at the end of the process, a comparison will be made between the image data that has been stored during training with a comparison image. Backpropagation ANN can work optimally if it is trained using input data that has determined the size, learning rate, parameters, and the number of nodes on the network. It is expected that the offered method can work optimally so that it produces an accurate percentage in order to minimize handwriting falcification.