Abstract Generative Adversarial Networks (GANs) are increasingly used in biometric systems. However, existing signature studies predominantly focus on strengthening discriminators or producing data for augmentation, leaving the quality and spoofing capability of generated forgeries insufficiently examined. To address this research gap, we propose Block-Induced Signature GAN (BISGAN )—a generator- focused architecture integrating inception-style blocks and attention mechanisms to preserve influential biometric features during forgery generation. We further introduce a train-shift learning strategy, grounded in adversarial robustness theory and the Resource-Based View (RBV), which enhances the generator’s ability to mimic authentic signature traits. Experiments on benchmark datasets demonstrate that BISGAN achieves 88%–100% spoofing success, exceeding prior GAN-based approaches by at least 12%. To support objective assessment, we develop a Generated Quality Metric (GQM) that evaluates forgery realism using latent feature distribution distances. The results confirm the importance of generator-centric adversarial modeling for advancing the robustness and security evaluation of signature verification systems.

Exponential growth of genome-wide assays of gene expressions and their public access open new horizons for machine learning methodologies to effectively perform genetic analysis. In this work, domain specific pre-train k-mer embeddings of DNA sequences are generated by utilising FastText approach. Sequence co-expression pattern information is embedded into 200 dimensional vectors by training Fasttext model on 317,151 samples of DNA sequences (with k-mers representation). We propose a novel idea to utilize the information of various codons present in amino acids for the evaluation of learned sequence vectors. We employ two diverse techniques to compare the performance of generated task-specific k-mer embeddings with state-of-the-art publicly available generic k-mer embeddings of genome. Firstly, we utilize a dimensionality reduction approach namely PCA to alleviate the dimensions of DNA sequences upto 50 features by preserving almost 85% of sequence features information. Afterwards, TSNE algorithms is used to visualize k-mer embeddings and to make sure whether different codons representing the same amino acid are more closer to each other than the ones representing different amino acids. Secondly, to assess the analogy of k-mer embeddings, generated domain specific k-mer embeddings are compared with state-of-the-art k-mer embeddings by estimating the cosine similarity among those codons vectors which represent same amino acid. Overall, we believe that task-specific distributed representation of k-mers would be useful for DNA methylation and Histone occupancy prediction tasks.

Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD). A few small datasets that are available for research community usually suffer from impractical image capturing conditions and stringent inclusion criteria. These shortcomings in already limited choice of existing datasets make it challenging to mature a CAD system so that it can perform in real-world environment. In this paper we present a large publicly available retinal fundus image dataset for glaucoma classification called G1020. The dataset is curated by conforming to standard practices in routine ophthalmology and it is expected to serve as standard benchmark dataset for glaucoma detection. This database consists of 1020 high resolution colour fundus images and provides ground truth annotations for glaucoma diagnosis, optic disc and optic cup segmentation, vertical cup-to-disc ratio, size of neuroretinal rim in inferior, superior, nasal and temporal quadrants, and bounding box location for optic disc. We also report baseline results by conducting extensive experiments for automated glaucoma diagnosis and segmentation of optic disc and optic cup.

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.

Text document classification is an important task for diverse natural language processing based applications. Traditional machine learning approaches mainly focused on reducing dimensionality of textual data to perform classification. This although improved the overall classification accuracy, the classifiers still faced sparsity problem due to lack of better data representation techniques. Deep learning based text document classification, on the other hand, benefitted greatly from the invention of word embeddings that have solved the sparsity problem and researchers focus mainly remained on the development of deep architectures. Deeper architectures, however, learn some redundant features that limit the performance of deep learning based solutions. In this paper, we propose a two stage text document classification methodology which combines traditional feature engineering with automatic feature engineering (using deep learning). The proposed methodology comprises a filter based feature selection (FSE) algorithm followed by a deep convolutional neural network. This methodology is evaluated on the two most commonly used public datasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7.7% on 20 Newsgroups and 6.6% on BBC news datasets.

This paper presents a novel two-stream approach for document image classification. The proposed approach leverages textual and visual modalities to classify document images into ten categories, including letter, memo, news article, etc. In order to alleviate dependency of textual stream on performance of underlying OCR (which is the case with general content based document image classifiers), we utilize a filter based feature-ranking algorithm. This algorithm ranks the features of each class based on their ability to discriminate document images and selects a set of top 'K' features that are retained for further processing. In parallel, the visual stream uses deep CNN models to extract structural features of document images.Finally, textual and visual streams are concatenated together using an average ensembling method. Experimental results reveal that the proposed approach outperforms the state-of-the-art system with a significant margin of 4.5% on publicly available Tobacco-3482 dataset.

This paper presents a novel approach, called AirScript, for creating, recognizing and visualizing documents in air. We present a novel algorithm, called 2-DifViz, that converts the hand movements in air (captured by a Myo-armband worn by a user) into a sequence of x, y coordinates on a 2D Cartesian plane, and visualizes them on a canvas. Existing sensor-based approaches either do not provide visual feedback or represent the recognized characters using prefixed templates. In contrast, AirScript stands out by giving freedom of movement to the user, as well as by providing a real-time visual feedback of the written characters, making the interaction natural. AirScript provides a recognition module to predict the content of the document created in air. To do so, we present a novel approach based on deep learning, which uses the sensor data and the visualizations created by 2-DifViz. The recognition module consists of a Convolutional Neural Network (CNN). and two Gated Recurrent Unit (GRU) Networks. The output from these three networks is fused to get the final prediction about the characters written in air. AirScript can be used in highly sophisticated environments like a smart classroom, a smart factory or a smart laboratory, where it would enable people to annotate pieces of texts wherever they want without any reference surface. We have evaluated AirScript against various well-known learning models (HMM, KNN, SVM, etc.) on the data of 12 participants. Evaluation results show that the recognition module of AirScript largely outperforms all of these models by achieving an accuracy of 91.7% in a person independent evaluation and a 96.7% accuracy in a person dependent evaluation.

Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to the reader in a structured manner. It is a hard problem due to varying layouts and encodings of the tables. Researchers have proposed numerous techniques for table detection based on layout analysis of documents. Most of these techniques fail to generalize because they rely on hand engineered features which are not robust to layout variations. In this paper, we have presented a deep learning based method for table detection. In the proposed method, document images are first pre-processed. These images are then fed to a Region Proposal Network followed by a fully connected neural network for table detection. The proposed method works with high precision on document images with varying layouts that include documents, research papers, and magazines. We have done our evaluations on publicly available UNLV dataset where it beats Tesseract's state of the art table detection system by a significant margin.

B This paper presents a novel approach, named D-StaR, for stamp segmentation from scanned document images. The presented approach is generic (applicable to stamps of any color, shape, size, and orientation) and based on deep learning. In particular, it uses Fully Convolutional networks for semantic analysis of documents to extract stamps. The presented approach is evaluated on a publicly available stamp dataset. Evaluation results show that the presented approach outperforms the state-of-the-art methods for stamp segmentation and achieves pixel based precision and recall of 87% and 84%, respectively. Deeper analysis of the evaluation reveals that the presented approach can segment both overlapping and non-overlapping stamps, which was always a problem for existing systems in the literature.

In this paper, we propose a method for automatic signature segmentation using hyper-spectral imaging. The proposed method first uses the connected component analysis and local features to segment the printed text and signatures. Secondly, it uses spectral response of text, signature, and background to extract signature pixels. The proposed method is robust, and remains unaffected by color and intensity of the ink, and by any structural information of the text, as the classification relies exclusively on the spectral response of the document. The proposed method can extract signature pixels either overlapping or non-overlapping from different backgrounds like, logos, tables, stamps, and printed text. We used high-resolution hyper-spectral imaging to study and classify 300 documents with varying backgrounds. We evaluated the proposed classification method and compared results with the state-of-the art system. The proposed method outperformed the state-of-the-art system and achieved 100% precision and 84% recall.

This paper presents a deep Convolutional Neural Network (CNN) based approach for document image classification. One of the main requirement of deep CNN architecture is that they need huge number of samples for training. To overcome this problem we adopt a deep CNN which is trained using big image dataset containing millions of samples i.e., ImageNet. The proposed work outperforms both the traditional structure similarity methods and the CNN based approaches proposed earlier. The accuracy of the proposed approach with merely 20 images per class outperforms the state-of-the-art by achieving classification accuracy of 68.25%. The best results on Tobbacoo-3428 dataset show that our proposed method outperforms the state-of-the-art method by a significant margin and achieved a median accuracy of 77.6% with 100 samples per class used for training and validation.

This paper presents the results of the ICDAR 2015 competition on signature verification and writer identification for on- and off-line skilled forgeries jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. Two modalities (signatures and handwritten text) are considered and training and evaluation data are collected and provided by FHEs and PR-researchers. Four tasks are defined for four different languages; Bengali off-line signature verification, Italian off-line signature verification, German on-line signature verification, and English handwritten text based writer identification. In total, 40 systems have participated in this competition. The participants of the signatures modality were motivated to report their results in Likelihood Ratios (LRs). This has made the systems even more interesting for application in forensic casework. For evaluating the performance of the systems, we have used the forensically substantial Cost of Log Likelihood Ratios (Ĉllr) in the case of signatures, and the F-measure in the case of handwritten text.

Online signature verification methods examine the dynamics of the handwriting process to decide whether a signature is probably genuine or forged. Most of the previously proposed methods for online signature verification apply Neural Networks, Dynamic Time Warping, or Hidden Markov Model for classification and they consider several aspects, like planar coordinates, pressure, velocity, and acceleration with respect to time. Here we apply a non-parametric statistical test for a comparison of features and the verification of signatures.

The purpose of writing this paper is two-fold. First, it presents a novel signature stability analysis based on signature's local / part-based features. The Speeded Up Local features (SURF) are used for local analysis which give various clues about the potential areas from whom the features should be exclusively considered while performing signature verification. Second, based on the results of the local stability analysis we present a novel signature verification system and evaluate this system on the publicly available dataset of forensic signature verification competition, 4NSigComp2010, which contains genuine, forged, and disguised signatures. The proposed system achieved an EER of 15%, which is considerably very low when compared against all the participants of the said competition. Furthermore, we also compare the proposed system with some of the earlier reported systems on the said data. The proposed system also outperforms these systems.

In this paper we compare two captured databases which contain local acceleration and angle information recorded during the signing process. Approximately a year passed between the capturing of the two databases and they contain several signatures from the same writers. We analyze the expedience of the proposed devices and examine the overlap of the databases using Legendre approximation for feature computation and Support Vector Machine for classification. In addition we plan to make the concerned databases publicly available for research purposes.

This paper presents a novel signature verification system based on local features of signatures. The proposed system uses Fast Retina Key points (FREAK) which represent local features and are inspired by the human visual system, particularly the retina. To locate local points of interest in signatures, two local key point detectors, i.e., Features from Accelerated Segment Test (FAST) and Speeded-up Robust Features (SURF), have been used and their performance comparison in terms of Equal Error Rate (EER) and time is presented. The proposed system has been evaluated on publicly available dataset of forensic signature verification competition, 4NSigComp2010, which contains genuine, forged, and disguised signatures. The proposed system achieved an EER of 30%, which is considerably very low when compared against all the participants of the said competition. In addition to EER, the proposed system requires only 0.6 seconds on average to verify a 3000*1500 scanned signature. This shows that the proposed system has a potential and suitability for forensic signature verification as well as real time applications.

This paper presents the results of the ICDAR2013 competitions on signature verification and writer identification for on- and offline skilled forgeries jointly organized by PR researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. Two modalities (signatures, and handwritten text) are considered where training and evaluation data (in Dutch and Japanese) were collected and provided by FHEs and PR-researchers. Four tasks were defined where the systems had to perform Dutch offline signature verification, Japanese offline signature verification, Japanese online signature verification, and Dutch writer identification. The participants of the signatures modality were motivated to report their results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic casework. For evaluation of signatures modality, we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Ĉllr). The system having the smallest value of the Minimum Cost of Log Likelihood Ratio (Ĉllrmin) is declared winner. For evaluation of the handwritten text modality, we used the precision and accuracy measures and winners are announced on the basis of best F-measure value.

The purpose of writing this paper is three-fold. First, it presents a novel local / part-based automatic system for forensic signature verification involving disguised signatures. Disguised signatures are written by authentic authors but with the intention of later denial. The proposed system reaches an equal error rate of 3.36% in classifying disguised and genuine signatures. Second, it compares the performance of the proposed system with various state-of-the-art signature verification systems on the same data, i.e., the publicly available dataset of 4NSigComp2010 signature verification competition. Third, it presents a performance comparison of the proposed system with human forensic handwriting examiners. It is important as it highlights the potential of the proposed system to assist humans in solving real world forensic signature verification cases.

In this paper we present a framework for real-time online signature verification scenarios. The proposed framework is based on state-of-the-art feature extraction and Gaussian Mixture Model (GMM) classification. While our signature verification library is generally applicable to any input device using digital pens, we have implemented verification scenarios using the Anoto digital pen. As such our automated signature verification framework becomes an interesting commodity for industry, because the Anoto SDK is easy to apply and the GMM-based classification can be seamlessly integrated. The novelty of this work is the application of our framework that takes real-time online signature verification to every scenario where digital pens may potentially be used. In this paper we describe several scenarios where our framework has been applied, including signatures in financial contracts or ordering processes. We also propose a general approach to integrate the GMM-descriptions into electronic ID-cards in order to also store behavioral biometrics on these cards. In experiments we have measured the performance of the signature verification system when skilled forgeries were present. The interest shown by our partner financial institutions and the results of our initial evaluations indicate that our signature verification framework suits exactly the demands of our clients.

In this paper we propose a novel method for the extraction of signatures from document images. Instead of using a human defined set of features a part-based feature extraction method is used. In particular, we use the Speeded Up Robust Features (SURF) to distinguish the machine printed text from signatures. Using SURF features makes the approach generally more useful and reliable for different resolution documents. We have evaluated our system on the publicly available Tobacco-800 dataset in order to compare it to previous work. Finally, all signatures were found in the images and less than half of the found signatures are false positives. Therefore, our system can be applied for practical use.

This paper presents the results of the ICFHR2012 Competition on Automatic Forensic Signature Verification jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. A forensic like training set containing disguised signatures along with skilled forgeries and genuine signatures was provided to the participants. They were motivated to report the results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic casework. For evaluation we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Ĉllr). The system having the best Minimum Cost of Log Likelihood Ratio ( Ĉllrmin) is declared winner. Various experiments both including and excluding disguised signatures from the test set are reported.

This paper is an effort towards the development of a shared conceptualization regarding automatic signature verification systems. The requirements of both communities, Pattern Recognition and Forensic Handwriting Examiners, are explicitly focused. This is required because an increasing gap regarding evaluation of automatic verification systems is observed in the recent past. The paper addresses three major areas. First, it highlights how signature verification is taken differently in the above mentioned communities and why this gap is increasing. Various factors that widen this gap are discussed with reference to some of the recent signature verification studies and probable solutions are suggested. Second, it discusses the state-of-the-art evaluation and its problems as seen by FHEs. The real evaluation issues faced by FHEs, when trying to incorporate automatic signature verification systems in their routine casework, are presented. Third, it reports a standardized evaluation scheme capable of fulfilling the requirements of both PR researchers and FHEs.

The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic casework. In collaboration with the German Research Center for Artificial Intelligence we have organized a signature verification competition on datasets with two scripts (Dutch and Chinese) in which we asked to compare questioned signatures against a set of reference signatures. We have received 12 systems from 5 institutes and performed experiments on online and offline Dutch and Chinese signatures. For evaluation, we applied methods used by Forensic Handwriting Examiners (FHEs) to assess the value of the evidence, i.e., we took the likelihood ratios more into account than in previous competitions. The data set was quite challenging and the results are very interesting.

This competition scenario aims at a performance comparison of several automated systems for the task of signature verification. The systems have to rate the probability of authorship and non-authorship of signatures. In particular they have to determine whether questioned signatures are simulated disguised or the normal signature of the reference writer. Furthermore, the results will be compared to forensic handwriting examiners (FHEs) opinions on the same tasks. As such, to the best of the authors’ knowledge, this scenario will be the first attempt in literature to relate system performances to the performance of FHEs who gave their opinion on exactly the the same signatures.