Real-time 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 un-ruled 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 multi-shape components based on distance, shape and mutual position analysis of components. Furthermore, iterative k-means 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 state-of-the-art methods.

Online signature verification (OSV) is one of the most challenging tasks in writer identification and digital forensics. Owing to large intra-individual variability, there is a critical requirement to accurately learn the intrapersonal variations of the signature to achieve higher classification accuracy. To achieve this, in this paper, we propose an OSV framework based on deep convolutional Siamese network (DCSN). DCSN automatically extract robust feature descriptions based on metric-based loss function which decreases intra-writer variability (Genuine-Genuine) and increase inter-individual variability (Genuine-Forgery) and guides the DCSN for effective discriminative representation learning for online signatures. Experiments conducted on three widely accepted datasets MCYT-100 (DB1), MCYT-330 (DB2) and SVC-2004-Task2 emphasize the capability of our framework to distinguish the genuine and forgery samples. Experimental results confirm the efficiency of the proposed DCSN in one shot learning by achieving a lower error rate as compared to many recent and state-of-the art OSV models.

Handwriting based Gender identification is challenging due to unconstrained handwriting and individual differences in writing. To solve this problem, we propose a new adaptive multi-gradient of Sobel kernels for extracting Adaptive Multi-Gradient Features (AMGF). For extracted text lines, the proposed method finds dominant pixels based on directional symmetry of text pixels given by AMGF. We perform histogram operation for adaptive multi-gradient values extracted corresponding to dominant pixels. The gradient values that give the highest peak in respective histograms is chosen as features. This results in feature vector having four AMGF values. The same vector are generated for successive text lines in each image to study either consistency, which is expected for females or inconsistency, which is expected for males in writing styles. The correlation is estimated based on feature vectors of the first and the successive text lines until converging or diverging criteria is met. If convergence happens, the input document is considered as female else is considered as male. The method is tested on our own dataset, which includes large variations and standard datasets, namely, QUWI, IAM-1+IAM-2 and KHATT, to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms the existing methods.

As technology advances to make living comfortable for people, at the same time, different crimes also increase. One such sensitive crime is creating fake International Mobile Equipment Identity (IMEI) for smart mobile devices. In this paper, we present a new fusion based method using R, G and B color components for detecting forged IMEI numbers. To the best of our knowledge, this is the first work for forged IMEI number detection in mobile images. The proposed method first finds variances for R, G and B images of a forged input image to study local changes. The variances are used to derive weights for respective color components. The same weights are convolved with respective pixel values of R, G and B components, which results in the fused image. For the fused image, the proposed method extracts features based on sparsity, the number of connected components, and the average intensity values for edge components in respective R, G and B components, which gives six features. The proposed method finds absolute difference between fused and input images, which gives feature vector containing six difference values. The proposed method constructs templates based on samples chosen randomly. Feature vectors are compared with the templates for detecting forged IMEI numbers. Experiments are conducted on our own dataset and standard datasets to evaluate the proposed method. Furthermore, comparative studies with the related existing methods show that the proposed method outperforms the existing methods.

Text line segmentation from handwritten documents is challenging when a document image contains severe touching. In this paper, we propose a new idea based on Weighted-Gradient Features (WGF) for segmenting text lines. The proposed method finds the number of zero crossing points for every row of Canny edge image of the input one, which is considered as the weights of respective rows. The weights are then multiplied with gradient values of respective rows of the image to widen the gap between pixels in the middle portion of text and the other portions. Next, k-means clustering is performed on WGF to classify middle and other pixels of text. The method performs morphological operation to obtain word components as patches for the result of clustering. The patches in both the clusters are matched to find common patch areas, which helps in reducing touching effect. Then the proposed method checks linearity and non-linearity iteratively based on patch direction to segment text lines. The method is tested on our own and standard datasets, namely, Alaei, ICDAR 2013 robust competition on handwriting context and ICDAR 2015-HTR, to evaluate the performance. Further, the method is compared with the state of art methods to show its effectiveness and usefulness.

Gender identification based on handwriting analysis has received a special attention to researchers in the field of document image analysis as it is useful for several real-time applications like forensic, population counting, etc. In this paper, we explore Multi-Gradient Directional (MGD) features, which provide direction of dominant pixels obtained by Canny edge image, and gradient direction symmetry. The proposed method further performs histogram operation for gradient angle information of dominant pixels of respective multi-gradient directional images to select angles, which contribute to the highest peak. This results in feature vectors. The process of feature vector formation continues for the segmented first, second, and third text lines in each image by male or female. Next, correlation is estimated for the vector of the first line with successive lines until converging or diverging criteria is met. If the convergence happens, a document is considered as by female, else is considered as by male. The method is tested on our own dataset, which includes images of different scripts, writers, papers, pens, and ages, and the standard database QUWI which includes Arabic and English texts, to demonstrate the efficiency of the proposed method. Comparative studies with the state of the art methods show that the proposed method is effective and useful.

The presence of both graphics and scene text in video frames makes text detection and recognition problem more challenging because the nature of the two texts differs significantly. This paper aims to propose a novel method for separation of graphics and scene text to achieve good recognition rate based on the fact that Canny and Sobel edge pattern share common property for text. We propose to use Ring Radius Transform to identify the radius that represents the medial axis in the edge image. We study the intra relationship between bins of the histograms over respective radius values, resulting in intra line graphs. In this way, the method finds intra line graphs for both Canny and Sobel edge images of the input text lines. To identify the unique distribution for separation of graphics and scene texts, we explore the inter relationship between intra line graphs of Canny and Sobel edge image with respective medial axes values. This results in Gaussian distribution for graphics and non-Gaussian for scene text. Experimental results on horizontal, non-horizontal, different scripts etc. show that the proposed method is effective for classification and the results of baseline recognition methods show that recognition rate is significantly improved after classification.

Word segmentation from video text line is challenging because video poses several challenges, such as complex background, low resolution, arbitrary orientation, etc. Besides, word segmentation is essential for improving text recognition accuracy. Therefore, we propose a novel method for segmenting words by exploring zero crossing points for each sliding window over text line. The candidate zero crossing pointes are defined based on characteristics of positive and negative Laplacian values at text region and non-text region. The percentage of candidate zero crossing points is calculated for each sliding window and is used for identifying the seed window that represents space between words. For the seed window, we propose a novel idea of horizontal and vertical sampling based on the percentage values to estimate the width and the height of the word spacing. Then the width and the height of the word spacing are used to validate the actual word spacing. Experimental results comparing with an existing method show that the proposed method is better than the existing method in terms of recall, precision and f-measure on curved, horizontal, non-horizontal, Hua's video data, as well as ICDAR data. We also test it on our own data containing multiscript text lines to show the robustness of the proposed method.

In this paper, we address curved text detection in video through a new enhancement criterion and the use of quad tree. The proposed method makes use of the quad tree to simplify the task of handling the entire frame at each stage. The proposed method employs a novel criterion for grouping of pixels based on their R, G and B values to enhance text information. As generally, a text detection problem is a two class problem, we used k-means with k=2 to identify potential text candidate pixels. From these potential candidates, connected components are then extracted and subjected to further analysis, where symmetry property based on stroke width is used for further authentication of the text representatives. These authenticated text representatives are then exploited as seed points to restore the text information with reference to the Sobel edge frame of the original input frame. To preserve the spatial information of text pixels the concept of quad tree is applied. From these seed blocks, text lines are extracted by the use of a region growing approach driven completely based on Sobel edge map. The proposed method is tested on curved video data and Hua's horizontal video text data in terms of recall, precision, f-measure, misdetection rate and processing time. The results are compared and analyzed to show that the proposed method outperforms several existing methods in terms of accuracy and efficiency.

In this paper, we propose a model for feature selection and indexing of online signatures based person identification. For representation of online signatures, a set of 100 global features of MCYT online signature database is considered. However, MCYT based features are high dimension features which significantly increases the response time and space requirements for signature identification process. To overcome this problem, multi cluster feature selection method is proposed to reduce the dimensionality by finding a relevant feature subset. Moreover, in some applications, where the database is supposed to be very large, the identification process typically has an unacceptably long response time. A solution to speed up the identification process is to design an indexing model prior to identification which reduces the number of candidate hypotheses to be considered during matching by the identification algorithm. Hence in this paper, Kd-tree based indexing model is designed for online signatures based person identification. The experimental results reveal that the proposed model works more efficiently both in terms of time and accuracy.

In this paper, a novel approach for detection of text and non-text regions in video frames is proposed. The proposed approach performs block wise eigen analysis on the gradient image of the video frame. For each block of the gradient frame, the dominant eigen value is computed to decide if the block could be a candidate text block. The K-means clustering is then applied to further identify text blocks among the candidate blocks. From each of the identified candidate text blocks edges are extracted using the sobel operator, and then by the use of horizontal and vertical profiles a bounding rectangle is fixed up. Further, geometric properties of the identified text regions are studied to eliminate false text regions. In order to validate the efficacy of the proposed approach, experimentation on a dataset containing 800 video frames has been carried out. The obtained results ensure that the proposed approach is with increased text detection rate with very low false and misdetection rates when compared to the other existing state of the art techniques.

In this paper, we propose a new approach for symbolic representation of off-line signatures based on relative distances between centroids useful for verification. Distances between centroids of off-line signatures are used to form an interval valued symbolic feature vector for representing signatures. A method of off-line signature verification based on the symbolic representation is presented. We investigate the feasibility of the proposed representation scheme for signature verification on a MCYT_ signature database. We cluster similar signatures in each class and also investigate the cluster based symbolic representation for signature verification. Unlike other signature verification methods, the proposed method is simple and efficient. Several experiments are conducted to demonstrate the efficacy of the proposed scheme.

In this paper, we propose a new method of representing on-line signatures by interval valued symbolic features. Global features of on-line signatures are used to form an interval valued feature vectors. Methods for signature verification and recognition based on the symbolic representation are also proposed. We exploit the notions of writer dependent threshold and introduce the concept of feature dependent threshold to achieve a significant reduction in equal error rate. Several experiments are conducted to demonstrate the ability of the proposed scheme in discriminating the genuine signatures from the forgeries. We investigate the feasibility of the proposed representation scheme for signature verification and also signature recognition using all 16500 signatures from 330 individuals of the MCYT bimodal biometric database. Further, extensive experimentations are conducted to evaluate the performance of the proposed methods by projecting features onto Eigenspace and Fisherspace. Unlike other existing signature verification methods, the proposed method is simple and efficient. The results of the experimentations reveal that the proposed scheme outperforms several other existing verification methods including the state-of-the-art method for signature verification.

In this paper we employ appearance based models for document script identification. They are employed to identify scripts at both paragraph and word level. Elaborate experimentation has been conducted which has revealed that they are robust enough to handle highly confusing scripts and their performance does not degrade drastically even in the presence of noise. A generic script identification has been attempted, to identify both Asian and European scripts by considering a dataset of twenty different languages.

In this paper, a novel method of representing symbolic images in a symbolic image database (SID) invariant to image transformations that is useful for exact match retrieval is presented. The relative spatial relationships existing among the components present in an image are perceived with respect to the direction of reference and preserved by a set of triples. A distinct and unique key is computed for each distinct triple. The mean and standard deviation of the set of keys computed for a symbolic image are stored along with the total number of keys as the representatives of the corresponding image. The proposed exact match retrieval scheme is based on a modified binary search technique and, thus, requires O (logn) search time in the worst case, where n is the total number of symbolic images in the SID. An extensive experimentation on a large database of 22,630 symbolic images is conducted to corroborate the superiority of the model. The effectiveness of the proposed representation scheme is tested with standard testbed images