Age Estimation using Disconnectedness Features in Handwriting Realtime applications of handwriting analysis have increased drastically in the fields of forensic and information security because of accurate cues. One of such applications is human age estimation based on handwriting for the purpose of immigrant checking. In this paper, we have proposed a new method for age estimation using handwriting analysis using Hu invariant moments and disconnectedness features. To make the proposed method robust to both ruled and unruled documents, we propose to explore intersection point detection in Canny edge images of each input document, which results in text components. For each text component pair, we propose Hu invariant moments for extracting disconnectedness features, which in fact measure multishape components based on distance, shape and mutual position analysis of components. Furthermore, iterative kmeans clustering is proposed for the classification of different age groups. Experimental results on our dataset and some standard datasets, namely, IAM and KHATT, show that the proposed method is effective and outperforms the stateoftheart methods.

Cross multiscale locally encoded gradient patterns for offline textindependent writer identification Abstract   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 offline system for textindependent 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 welldesigned and defined feature extraction method improves the classification task. This paper proposes, for feature extraction, an effective, yet highquality and conceptually simple feature image descriptor referred to as Cross multiscale 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 subimages) 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 (1NN). Our system is evaluated on six standard databases (IFNENIT, AHTIDMW, 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 stateoftheart methods, the proposed system achieves the highest performance on IFNENIT, AHTIDMW, and ICDAR2011 databases, and demonstrates competitive performance on IAM, CVL, and Firemaker databases.