Text-independent Writer Identification Research Articles

Offline writer identification approach using moment features and high-order correlation functions

For text-independent writer identification(WI) tasks, this paper proposes a novel method based on semantic codebooks and two effective feature descriptors. This method requires constructing semantic codebooks consisting of various subimages and their labels. Moment features and high-order correlation functions are used to describe handwriting style, and the standard Euclidean distance function is used for distance measurement. Then, two-way analysis of variance(TW-ANOVA) is used to filter out character factors that affect identification accuracy. Finally, a combined measure obtained from multiple classifiers based on a fuzzy integral rule is used to determine the candidate writers. To verify the effectiveness of the system, experiments are performed using the Firemaker, IAM, CRUG-CN and Uyghur2016 databases. The evaluation results demonstrate better performance than most existing methods.

DT2F-TLNet: A novel text-independent writer identification and verification model using a combination of deep type-2 fuzzy architecture and Transfer Learning networks based on handwriting data

Identifying and verifying the identity of people based on scanned images of handwritten documents is an applicable biometric modality with applications in forensic and historic document investigation, and it is an important study area within the research field of behavioral biometrics. Despite this, there are few studies in this field. Furthermore, there are very few standard datasets for identifying and verify handwritten documents. Also, handwritten documents lose their character during time because of ink spread and drying. Therefore, it is necessary to provide a method that can identify and verify handwritten documents under various uncertainties. In this study, a text-independent writer identification and verification model in offline state under different experimental conditions is developed using a combination of Deep Type-2 Fuzzy architecture and Transfer Learning networks (DT2F-TLNet). So, a right-to-left dataset has been collected. The proposed DT2F-TLNet model is validated using both the designed dataset and other benchmark datasets. The proposed model is distinguished by the fact that it is developed to be independent of the textual content of the handwritten cases and can be used for various languages. The study's findings show that the developed DT2F-TLNet model can learn properties from heterogeneous handwriting data and results in higher accuracy than other comparable approaches.

Offline writer identification using deep feature concatenation

Handwriting is an individual trait that serves as evidence to authenticate a particular writer. Identifying the writer of a handwritten text has shown encouraging results in examining historical and forensic documents. In this paper, we propose a novel offline writer identification system based on the challenging analysis of small amount of data to extract distinct patterns. In our deep network, the feature extraction process relies on a specially designed dual-path architecture, and the resulting embeddings are concatenated to produce the final learned features. To deal with a variety of uncertainties such as high intra-class variations and noises, we leverage the fuzzy logic in the design of a custom Convolutional Neural Network (CNN) with a type-2 fuzzy activation function for the first path. Additionally, the second path utilizes the transfer learning-based CNN to enhance the discriminability of the learned features. Our method allows for text-independent writer identification, eliminating the need for identical handwriting samples to train and test the model. Considering that various factors can influence the handwriting style, a dataset containing right-to-left handwriting samples is assembled. The proposed method is evaluated on our developed dataset and four widely-known public datasets, namely KHATT, CVL, Firemaker, and IAM. High accuracy values are achieved, with results of 99.85%, 99.83%, 99.79%, 99.64%, and 98.17% for each dataset, respectively. One noteworthy aspect of this study is that the evaluation results on diverse datasets demonstrate the applicability of the proposed model to various languages. Moreover, the model performs effectively in real-world scenarios with limited handwritten data.

An effective DeepWINet CNN model for off-line text-independent writer identification

An effective DeepWINet CNN model for off-line text-independent writer identification

Offline text-independent writer identification using a codebook with structural features.

Understanding handwritten documents is a vital and challenging problem that attracts many researchers in the fields of forensic and authentication science. This paper presents an offline system for text-independent writer identification of handwritten documents. The system extracts a handwritten connected component contour, which in turn is divided into segments of specific length. The system utilizes the concept of a bag of features in the writer recognition domain and considers handwritten contour segments to extract two conceptually simple and effective structural features. These features are the contour point curve angle and the CONtour point CONcavity/CONvexity. The system uses the proposed features to train a k-means clustering algorithm to construct a codebook of size K. The method then uses occurrence histograms of the extracted features in the codebook to create a final feature vector for each handwritten document. The effectiveness of the proposed features is evaluated in the writer identification domain using two widely used classification methods: the nearest neighbor and the support vector machine techniques. The proposed writer identification is evaluated on two large and public datasets from different language domains, the Arabic KHATT and English IAM datasets. The experimental results show that the proposed system outperforms state-of-the-art methods on the IAM dataset and provides competitive results on the KHATT dataset with respect to the identification rate.

Text Independent Writer Identification Based on Pre-training Model and Feature Fusion

Handwriting is one of the natural biological characteristics of human beings. People have a high degree of acceptance of identity recognition technology based on handwriting. It has a wide application prospect in the fields of finance, government affairs, justice and so on. In this paper, firstly, the original document image is segmented into smaller regional samples by using the sliding window method. Secondly, multiple pre-training models are used to extract the sample features, and multiple features of the samples are fused. Finally, the euclidean distance is used to express the degree of difference between samples. Experiments show that this method has a high recognition rate and has a certain application value.

WriterINet: a multi-path deep CNN for offline text-independent writer identification

WriterINet: a multi-path deep CNN for offline text-independent writer identification

Online Kanji Characters Based Writer Identification Using Sequential Forward Floating Selection and Support Vector Machine

Writer identification has become a hot research topic in the fields of pattern recognition, forensic document analysis, the criminal justice system, etc. The goal of this research is to propose an efficient approach for writer identification based on online handwritten Kanji characters. We collected 47,520 samples from 33 people who wrote 72 online handwritten-based Kanji characters 20 times. We extracted features from the handwriting data and proposed a support vector machine (SVM)-based classifier for writer identification. We also conducted experiments to see how the accuracy changes with feature selection and parameter tuning. Both text-dependent and text-independent writer identification were studied in this work. In the case of text-dependent writer identification, we obtained the accuracy of each Kanji character separately. We then studied the text-independent case by considering some of the top discriminative characters from the text-dependent case. Finally, another text-dependent experiment was performed by taking two, three, and four Kanji characters instead of using only one character. The experimental results illustrated that SVM provided the highest identification accuracy of 99.0% for the text-independent case and 99.6% for text-dependent writer identification. We hope that this study will be helpful for writer identification using online handwritten Kanji characters.

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.

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

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

Online Persian/Arabic Writer Identification using Gated Recurrent Unit Neural Networks

Conventional methods in writer identification mostly rely on hand-crafted features to represent the characteristics of different handwritten scripts. In this paper, we propose an end-to-end model for online text-independent writer identification on Persian/Arabic online handwritten scripts by using Gated Recurrent Unit (GRU) neural networks. The method does not require any specific knowledge for handwriting data analysis. Because of the exclusive ability of deep neural networks, we just represented our data by Random Strokes (RS) representations, which are differential horizontal and vertical coordinates extracted from different handwritings with a predefined length. This representation is a context independent representation. Therefore, this writer identification at RS level is more general than character level or word level in identification systems, which require character or word segmentation. The RS representation is then fed to a GRU neural network to represent the sequence for final classification. All RS features of a writer are then classified independently, and in the final stage, the posterior probabilities are averaged to make the final decision. Experiments on KHATT database, which consists of online handwritings of Arabic writers, gave us 100% accuracy on 10 writers and 76% accuracy on 50 writers, which is much better than previous works on online Persian/Arabic writer identification.

A deep learning framework for text-independent writer identification

Handwriting Writer Identification (HWI) refers to the process of handwriting text image analysis to identify the authorship of the documents. It has yielded promising results in various applications, including digital forensics, criminal purposes, exploring the writer of historical documents, etc. The complexity of the text image, especially in images with various handwriting makes the writer identification difficult. In this work, we propose an end-to-end system that relies on a straightforward yet well-designed deep network and very efficient feature extraction, emphasizing feature engineering. Our system is an extended version of ResNet by conjugating deep residual networks and a new traditional yet high-quality handwriting descriptor towards handwriting analysis. Our descriptor analyzes the handwriting thickness as a preliminary and essential feature for human handwriting characteristics. Our approach can also provide text-independent writer identification that we do not need to have the same handwriting content for learning our model. The proposed approach is evaluated and achieved consistent results on four public and well-known datasets of IAM, Firemaker, CVL, and CERUG-EN. We empirically demonstrate that our conjugated network outperforms the original ResNet, and it can work well for real-world applications in which patches with few letters exist.

Local gradient full-scale transform patterns based off-line text-independent writer identification

Handwriting based writer identification is one of the reliable components of behavioral biometrics. A huge effort has been done in recent years to improve the writer identification performance. Our paper presents a new and effective off-line text-independent system for writer identification. Extracting features from handwriting substantially impacts the ability of the classification process to identify the query writers. With the use of suitable classifier, a well-designed and discriminative feature extraction improves the classification performance. For that, we introduce a discriminative yet simple feature method, referred to as Local gradient full-Scale Transform Patterns (LSTP). The proposed LSTP algorithm captures salient local writing structure at small regions of interest of the writing. These writing regions are termed as connected components. In the classification stage, we perform Hamming distance based NN classifier to compare and match LSTP feature vectors. The proposed framework is evaluated on 9 well-known handwritten benchmarks. Experimental results show high identification performance against the current state-of-the-art.

Writer identification with n ‐tuple direction feature from contour

This study introduces an effective solution for text-independent writer identification by generalising contour-hinge feature, which is called n-tuple direction feature. For extracting n-tuple direction feature, the authors first obtain all contours from connected components, then n + 1 points are considered on the contour with a certain distance apart, and next, the directions of the fragments connecting two successive points are computed. The n + 1 points move on the contour and the n-dimensional histogram of directions is computed. The proposed method is evaluated on large Farsi and English databases. A correct writer identification rate of 92.2% for English handwritings from 900 persons and 97.7% for Farsi handwritings from 600 persons are achieved. Comparison between the proposed method and other studies shows the promising performance and superiority of the proposed method.

Cross multi-scale locally encoded gradient patterns for off-line text-independent writer identification

Writer identification is experiencing a revival of activity in recent years and continues to attract great deal of attention as a challenging and important area of research in the field of forensic and authentication. In this work, we introduce a reliable off-line system for text-independent writer identification of handwritten documents. Feature engineering is an essential component of a pattern recognition system, which can enhance or decrease the classification performance. A well-designed and defined feature extraction method improves the classification task. This paper proposes, for feature extraction, an effective, yet high-quality and conceptually simple feature image descriptor referred to as Cross multi-scale Locally encoded Gradient Patterns (CLGP). The proposed CLGP feature extraction method, which is expected to better represent salient local writing structure, operates at small observation regions (i.e., connected component sub-images) of the writing sample. CLGP histogram feature vectors computed from all these observation regions in all writing samples are considered as classification inputs to identify query writers using the Nearest Neighbor Classifier (1-NN). Our system is evaluated on six standard databases (IFN/ENIT, AHTID/MW, CVL, IAM, Firemaker, and ICDAR2011) including handwritten samples in Arabic, English, French, Greek, German, and Dutch languages. Comparing the identification performance with old and recent state-of-the-art methods, the proposed system achieves the highest performance on IFN/ENIT, AHTID/MW, and ICDAR2011 databases, and demonstrates competitive performance on IAM, CVL, and Firemaker databases.

An effective and conceptually simple feature representation for off-line text-independent writer identification

Feature engineering forms an important component of machine learning and pattern recognition. It is a fundamental process for off-line writer identification of handwritten documents, which continues to be an interesting subject of research in various forensic and authentication areas. In this work, we propose an efficient, yet computationally and conceptually simple framework for off-line text independent writer identification using local textural features in characterizing the writing style of each writer. These include Local Binary Patterns (LBP), Local Ternary Patterns (LTP), and Local Phase Quantization (LPQ). Our approach focuses on exploiting the writing images at small observation regions where a set of connected component sub-images are cropped and extracted from each handwriting sample (document or set of word/text line images). These connected components are seen as texture images where each one of them is subjected to feature extraction using LBP, LPQ, or LTP. Then, a histogram sequence concatenation is applied to the feature image after dimensionality reduction followed by image subdivision into a number of non-overlapping regions. For classification, the 1-NN (Nearest Neighbor) classifier is used to identify the writer of the questioned samples based on the dissimilarity of feature vectors computed from all components in the writing. Experiments on IFN/ENIT (411 writers/Arabic), AHTID/MW (53 writers/Arabic), CVL (309 writers/English), and IAM (657 writers/English) databases demonstrate that our proposed system outperforms old and recent state-of-the-art writer identification systems on Arabic script, and demonstrates a competitive performance on English ones.

Text-independent writer identification using convolutional neural network

The text-independent approach to writer identification does not require the writer to write some predetermined text. Previous research on text-independent writer identification has been based on identifying writer-specific features designed by experts. However, in the last decade, deep learning methods have been successfully applied to learn features from data automatically. We propose here an end-to-end deep-learning method for text-independent writer identification that does not require prior identification of features. A Convolutional Neural Network (CNN) is trained initially to extract local features, which represent characteristics of individual handwriting in the whole character images and their sub-regions. Randomly sampled tuples of images from the training set are used to train the CNN and aggregate the extracted local features of images from the tuples to form global features. For every training epoch, the process of randomly sampling tuples is repeated, which is equivalent to a large number of training patterns being prepared for training the CNN for text-independent writer identification. We conducted experiments on the JEITA-HP database of offline handwritten Japanese character patterns. With 200 characters, our method achieved an accuracy of 99.97% to classify 100 writers. Even when using 50 characters for 100 writers or 100 characters for 400 writers, our method achieved accuracy levels of 92.80% or 93.82%, respectively. We conducted further experiments on the Firemaker and IAM databases of offline handwritten English text. Using only one page per writer to train, our method achieved over 91.81% accuracy to classify 900 writers. Overall, we achieved a better performance than the previously published best result based on handcrafted features and clustering algorithms, which demonstrates the effectiveness of our method for handwritten English text also.

A multi-language writer identification method based on image mining and genetic algorithm techniques

Writing identification based on handwriting has many applications in the real world. Due to various forms of written letters in different languages, one of the major challenges in this context is offering an efficient method not being dependent on any specific language. In this paper, we have proposed a new approach based on image mining techniques for offline and text independent writer identification. In this method, each writers’ prominent features are found from training samples, and then identification is done according to them. In the image mining part of the proposed approach, certain techniques including SVM classifier and genetic algorithms are employed. To evaluate this method and show its performance in different languages, CASIA for Chinese, IAM dataset for English and two datasets for Kannada and Persian language handwriting were examined. The experiment results demonstrate that the presented method has over 99% accuracy for these languages. Regarding the results in tested languages and the method details, it is highly likely that our method would have good results in other languages also.

An improved online writer identification framework using codebook descriptors

This work proposes a text independent writer identification framework for online handwritten data. We derive a strategy that encodes the sequence of feature vectors extracted at sample points of the temporal trace with descriptors obtained from a codebook. The derived descriptors take into account, the scores of each of the attributes in a feature vector, that are computed with regards of the proximity to their corresponding values in the assigned codevector of the codebook. A codebook comprises a set of codevectors that are pre-learnt by a k-means algorithm applied on feature vectors of handwritten documents pooled from several writers. In addition, for constructing the codebook, we consider features that are derived by incorporating a so called ‘gap parameter’ that captures characteristics of sample points in the neighborhood of the point under consideration. We formulate our strategy in a way that, for a given codebook size k, we employ the descriptors of only k−1 codevectors to construct the final descriptor by concatenation. The usefulness of the descriptor is demonstrated by several experiments that are reported on publicly available databases.

Block wise local binary count for off-Line text-independent writer identification

Feature engineering is fundamental in applied machine learning. It plays a major role in writer identification of handwritten documents, which has been an active area of research in the literature. In this paper, we propose a conceptually simple, yet high-quality and computationally efficient descriptor referred to as block wise local binary count (BW-LBC) for offline text independent writer identification of handwritten documents. The proposed BW-LBC operator characterizes the writing style of each writer by a set of histograms calculated from all the connected components in the writing. Each histogram is constructed by calculating the occurrence distribution of pixels corresponding to the writing within small blocks in each connected component extracted and cropped from the input handwriting sample (document or set of words/text lines). Specifically, for a given connected component divided into N × N non-overlapping blocks, the appearance probability of writing pixels in the block number i corresponds to the histogram bin number i in the produced corresponding histogram of N × N bins. The samples are classified according to their normalized histogram feature vectors through the nearest-neighbor rule (1-NN) using the Hamming distance. Extensive experiments performed on four challenging handwritten databases (IFN/ENIT, AHTID/MW, CVL and IAM) containing handwritten texts in Arabic and English languages, show that the proposed system using the BW-LBC operator demonstrates superior performance on the Arabic databases (i.e., AHTID/MW and IFN/ENIT) and competitive performance on the English scripts compared to the old and recent state-of-the-art writer identification approaches.