Abstract Source printer identification represents an interesting modality for document forgery detection. Establishing the identity of the printer that was employed to print a questioned document allows concluding its authenticity. This paper investigates the effectiveness of deep visual features (learned using convolutional neural networks) in characterization of the source printer. Images of printed documents are divided into small patches as well as characters for extraction of features. An off‐the‐shelf recognition engine is also integrated, allowing experiments in text‐dependent as well as text‐independent modes. Experiments are carried out on a standard data set of documents from 20 different printers and identification rates of 95.52% and 98.06% are reported using patches and characters, respectively. Furthermore, the discriminating power of different characters, as well as their combinations, is also being studied. Unlike many existing techniques, which rely on pre‐segmented characters and report results by comparing same characters only, the proposed technique works on complete images of printed documents and reports high identification rates.

Abstract To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns asbiomarkersof brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed handwriting representation and estimation methods presented in the literature and discusses their strengths and weaknesses. It also highlights the limitations of existing processes and the challenges commonly faced when designing such systems. High-level directions for further research conclude the paper.

Parkinson's disease (PD) is commonly characterized by several motor impairments like tremor, muscular rigidity and bradykinesia, that are collectively termed as `Parkinson's disease dysgraphia'. In an attempt to identify these motor-based Parkinsonian symptoms, experts have persistently been evaluating various dynamic attributes of handwriting, like pen pressure/position, stroke speed/trajectory, and on-surface/in-air time taken, captured with the help of online acquisition tools. Such devices not only capture various aspects of handwriting but provide rich sequential information that can be utilized to identify unique patterns from handwriting samples of PD patients. In this paper, we propose a model based on Bidirectional Gated Recurrent Units (BiGRU) to assess the potential of handwriting-based sequential information in the identification of Parkinsonian symptoms. One-dimensional convolution is applied to raw sequences and the resulting feature sequences are employed to train the BiGRU model for prediction. The results of our experiments validate the potential of our proposed technique in comparison to the state-of-the-art.

Digitization of historical manuscripts from premodern eras, has captivated the document analysis and pattern recognition community in recent years. Estimation of the period of production of such documents is a challenging yet favored research problem. In this paper, we present a deep learning based approach to effectively characterize the year of production of sample documents from the Medieval Paleographical Scale (MPS) dataset. By employing transfer learning on a number of popular pre-trained Convolutional Neural Network (CNN) models, we have significantly reduced the Mean Absolute Error (MAE) reported in previous studies.

Sketches and drawings are popularly employed in clinical psychology to assess the visual-motor and perceptual development in children and adolescents. Drawn responses by subjects are mostly characterized by high degree of deformations that indicates presence of various visual, perceptual and motor disorders. Classification of deformations is a challenging task due to complex and extensive rule representation. In this study, we propose a novel technique to model clinical manifestations using Deep Convolutional Neural Networks (DCNNs). Drawn responses of nine templates used for assessment of perceptual orientation of individuals are employed as training samples. A number of defined deviations scored in each template are then modeled by applying fine tuning on a pre-trained DCNN architecture. Performance of the proposed technique is evaluated on samples of 106 children. Results of experiments show that pre-trained DCNNs can model and classify a number of deformations across multiple shapes with considerable success. Nevertheless some deformations are represented more reliably than the others. Overall promising classification results are observed that substantiate the effectiveness of our proposed technique.

This paper presents a study on assessing the effectiveness of machine learned features to predict gender of writers from images of handwriting. Pre-trained Convolutional Neural Networks have been employed as feature extractors to discriminate male and female handwriting while classification is carried out using a number of classifiers, Linear Discriminant Analysis (LDA) being the most effective. Feature extraction is carried out by changing the scale of observation using word, patch and page images. Experiments are carried out on English and Arabic handwriting samples of the QUWI database and the realized results demonstrate the effectiveness of machine learned features in predicting gender from handwriting.

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes.

Prediction of gender and other demographic attributes of individuals from handwriting samples offers an interesting basic, as well as applied research problem. The correlation between gender and the visual appearance of handwriting has been validated by a number of studies and the present study is based on the same idea. We exploit the textural measurements as the discriminating attribute between male and female writings. The textural information in a writing is captured by applying a bank of Gabor filters to the image of handwriting. The mean and standard deviation values of the filter responses are collected in matrix and the Fourier transform of the matrix is used as a feature. Classification is carried out using a feed forward neural network. The proposed technique evaluated on a subset of the QUWI database realized promising results under different experimental settings.

Drawing tests have been long used by practitioners and researchers for early detection of psychological and neurological impairments. These tests allow subjects to naturally express themselves as opposed to an interview or a written assessment. Bender Gestalt Test (BGT) is a well-known and established neurological test designed to detect signs of perceptual distortions. Subjects are shown a number of geometric patterns for reconstruction and assessments are made by observing properties like rotation, angulations, simplification and closure difficulty. The manual scoring of the test, however, is a time consuming and lengthy procedure especially when a large number of subjects is to be analyzed. This paper proposes the application of image analysis techniques to automatically score a subset of hand drawn images in the BGT test. A comparison of the scores reported by the automated system with those assigned by the psychologists not only reveals the effectiveness of the proposed system but also reflects the huge research potential this area possesses.