Abstract Handwriting recognition and analysis has been an active area of research in the last two decades. Handwriting analysis is being studied in various fields of science, such as graphology, neurology, psychology, and computer science. Furthermore, automated handwriting analysis has several applications, including forensic, security, medical, and disease prediction. This paper presents the most recent handwriting analysis techniques and advancements available in the literature for age and gender classification/detection. Different steps, including feature extraction and classification, frequently used in the literature for age and gender detection, are discussed, and the presented works are classified according to the applied feature extraction and classification methods. The online and offline benchmark databases are also reviewed. We used a text mining technique to perform a quantitative content analysis of the presented research and better understand the co-occurrence network diagrams of age and gender classification/detection. This study is a valuable resource that provides new research directions to students and researchers interested in this field for further research and investigation.

Abstract Artificial Intelligent (AI) systems generally require training data of sufficient quantity and appropriate quality to perform efficiently. However, in many areas, such training data is simply not available or incredibly difficult to acquire. The recent developments in Virtual Reality (VR) have opened a new door for addressing this issue. This paper demonstrates the use of VR for generating training data for AI systems through a case study of human fall detection. Fall detection is a challenging problem in the public healthcare domain. Despite significant efforts devoted to introducing reliable and effective fall detection algorithms and enormous devices developed in the literature, minimal success has been achieved. The lack of recorded fall data and the data quality have been identified as major obstacles. To address this issue, this paper proposes an innovative approach to remove the afformentioned obstacle using VR technology. In this approach, a framework is, first, proposed to generate human fall data in virtual environments. The generated fall data is then tested with state-of-the-art visual-based fall detection algorithms to gauge its effectiveness. The results have indicated that the virtual human fall data generated using the proposed framework have sufficient quality to improve fall detection algorithms. Although the approach is proposed and verified in the context of human fall detection, it is applicable to other computer vision problems in different contexts, including human motion detection/recognition and self-driving vehicles.

With the rapid emergence of new technologies, a voluminous number of images including document images is generated every day. Considering the volume of data and complexity of processes, manual analysis, annotation, recognition, classification, and retrieval, of such document images is impossible. To automatically deal with such processes, many document image analysis applications exist in the literature and many of them are currently in place in different organisation and institutes. The performance of those applications are directly affected by the quality of document images. Therefore, a document image quality assessment (DIQA) method is of primary need to allow users capture, compress and forward good quality (readable) document images to various information systems, such as online business and insurance, for further processing. To assess the quality of document images, this paper proposes a new full-reference DIQA method using first followed by second order Hast derivations. A similarity map is then created using second order Hast derivation maps obtained by employing Hast filters on both reference and distorted images. An average pooling is then employed to obtain a quality score for the distorted document image. To evaluate the proposed method, two different datasets were used. Both datasets are composed of images with the mean human opinion scores (MHOS) considered as ground truth. The results obtained from the proposed DIQA method are superior to the results reported in the literature.

As digitised documents normally contain a large variety of structures, a page segmentation- and layout-free method for document image retrieval is preferable. In this research work, therefore, wavelet transform as a transform-based approach is initially used to provide different under-sampled images from the original image. Then, Gist operator, as a feature extraction technique, is employed to extract a set of global features from the original image as well as the sub-images obtained from the wavelet transform. Moreover, the column-wise variances of the values in each sub-image are computed and they are then concatenated to obtain another set of features. Considering each feature set, locality-sensitive hashing is employed to compute similarity distances between a query and the document images in the database. Finally, a classifier fusion technique using the mean function is taken into account to provide a document image retrieval result. The combination of these features and a clustering score fusion strategy provides higher document image retrieval accuracy. Two different databases of the document image are considered for experimentation. The results obtained from the experimental study are detailed and the results are encouraging.

Based on the hypothesis that a good / poor quality document image is most probably a readable / unreadable document, document image quality and readability have interchangeably been used in the literature. These two terms, however, have different meanings implying two different perspectives of looking at document images by human being. In document images, the level of quality and the degree of readability may have a relation / correlation considering human perception. However, to the best of our knowledge there is no specific study to characterise this relation and also validate the abovementioned hypothesis. In this work, at first, we created a dataset composed of mostly camera-based document images with various distortion levels. Each document image has then been assessed with regard to two different measures, the level of quality and the degree of readability, by different individuals. A detailed Normalised Cross Correlation analysis along with different statistical analysis based on Shapiro-Wilks and Wilcoxon tests has further been provided to demonstrate how document image quality and readability are linked. Our findings indicate that the quality and readability were somewhat different in terms of the population distributions. However, the correlation between quality and readability was 0.99, which implies document quality and readability are highly correlated based on human perception.

Text line segmentation in handwritten document image, as one of the preliminarily steps for document image recognition, is a challenging problem. In this paper, a hybrid method for text line extraction in handwritten document images is presented. Initially, a connected component (CC) labelling method following by a CC filtering is employed to extract a set of CCs from the input document image. A new distance measure is introduced to compute normal distances between the extracted CCs. By traversing the normal distance matrix from both the right and left directions, half-chains of CCs are constructed. The CCs half-chains are merged to obtain CCs full-chains. From the extracted full-chains separator lines are obtained. A gradient metric is proposed to detect and remove touching text lines. Using remaining separator lines the adaptive projection profile of the image is computed. Based on the projection profile, coarse text line extraction is performed. Finally, a fine text lines extraction is performed by applying a postprocessing step. To evaluate the method, two benchmarks named ICDAR2013 handwriting segmentation contest, and Kannada datasets composed of handwritten document images in English, Greek, Bengali, and Kannada languages were considered for experimentation. Experimental results indicate a promising performance was obtained compared to some of the state-of-the-art methods.

In this paper, an efficient offline signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of local binary pattern-based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual's handwritten signature class. A novel fuzzy similarity measure is further proposed to compute the similarity between a test sample signature and the corresponding interval-valued symbolic model for the verification of the test sample. To evaluate the proposed verification approach, a benchmark offline English signature data set (GPDS-300) and a large data set (BHSig260) composed of Bangla and Hindi offline signatures were used. A comparison of our results with some recent signature verification methods available in the literature was provided in terms of average error rate and we noted that the proposed method always outperforms when the number of training samples is eight or more.

In this paper, a full reference document image quality assessment (FR DIQA) method using texture features is proposed. Local binary patterns (LBP) as texture features are extracted at the local and global levels for each image. For each extracted LBP feature set, a similarity measure called the LBP similarity index (LBPSI) is computed. A weighting strategy is further proposed to improve the LBPSI obtained based on local LBP features. The LBPSIs computed for both local and global features are then combined to get the final LBPSI, which also provides the best performance for DIQA. To evaluate the proposed method, two different datasets were used. The first dataset is composed of document images, whereas the second one includes natural scene images. The mean human opinion scores (MHOS) were considered as ground truth for performance evaluation. The results obtained from the proposed LBPSI method indicate a significant improvement in automatically/accurately predicting image quality, especially on the document image-based dataset.

In this paper, a signature verification method based on texture features involving off-line signatures written in two different Indian scripts is proposed. Both Local Binary Patterns (LBP) and Uniform Local Binary Patterns (ULBP), as powerful texture feature extraction techniques, are used for characterizing off-line signatures. The Nearest Neighbour (NN) technique is considered as the similarity metric for signature verification in the proposed method. To evaluate the proposed verification approach, a large Bangla and Hindi off-line signature dataset (BHSig260) comprising 6240 (260×24) genuine signatures and 7800 (260×30) skilled forgeries was introduced and further used for experimentation. We further used the GPDS-100 signature dataset for a comparison. The experiments were conducted, and the verification accuracies were separately computed for the LBP and ULBP texture features. There were no remarkable changes in the results obtained applying the LBP and ULBP features for verification when the BHSig260 and GPDS-100 signature datasets were used for experimentation.

Due to the rapid increase of different digitized documents, the development of a system to automatically retrieve document images from a large collection of structured and unstructured document images is in high demand. Many techniques have been developed to provide an efficient and effective way for retrieving and organizing these document images in the literature. This paper provides an overview of the methods which have been applied for document image retrieval over recent years. It has been found that from a textual perspective, more attention has been paid to the feature extraction methods without using OCR.

Digitization of business processes and the use of mobile devices as portable scanner lead to a massive production of document images that is beyond manual handling. In such a scenario, automatic estimation of document image quality is a concern in order to adapt as early as possible document image analysis methods. In this paper, a method for full reference document image quality assessment (DIQA) using mainly foreground information is proposed. In the proposed method, a segmentation technique is employed on a reference document image to approximately separate foreground and background information. Foreground information of the document image are then considered in the form of foreground patches for computing image quality. For each foreground patch, corresponding gradient maps, obtained from the reference and distorted gradient magnitude maps, are used to compute a gradient magnitude similarity map of the patch. Gradient magnitude similarity deviation of the patch is then calculated by the means of standard deviation over all the values in the gradient magnitude similarity map obtained for the patch. An average pooling is finally performed on all the standard deviations obtained for all the foreground patches to obtain the final image quality metric of the distorted document image. To evaluate the proposed method, we used 3 different datasets. The first dataset was a dataset composed of 377 document images of which 29 were reference images and 348 were distorted images. The other datasets were LIVE and CSIQ datasets composed of scene images with MHOS as ground truth. The results obtained from the proposed system are encouraging.

The objective of this investigation is to present an interval-symbolic representation based method for offline signature verification. In the feature extraction stage, Connected Components (CC), Enclosed Regions (ER), Basic Features (BF) and Curvelet Feature (CF)-based approaches are used to characterize signatures. Considering the extracted feature vectors, an interval data value is created for each feature extracted from every individual's signatures as an interval-valued symbolic data. This process results in a signature model for each individual that consists of a set of interval values. A similarity measure is proposed as the classifier in this paper. The interval-valued symbolic representation based method has never been used for signature verification considering Indian script signatures. Therefore, to evaluate the proposed method, a Hindi signature database consisting of 2400 (100×24) genuine signatures and 3000 (100×30) skilled forgeries is employed for experimentation. Concerning this large Hindi signature dataset, the highest verification accuracy of 91.83% was obtained on a joint feature set considering all four sets of features, while 2.5%, 13.84% and 8.17% of FAR (False Acceptance Rate), FRR (False Rejection Rate), and AER (Average Error Rate) were achieved, respectively.

In this paper, a complete logo detection/ recognition system for document images is proposed. In the proposed system, first, a logo detection method is employed to detect a few regions of interest (logo-patches), which likely contain the logo(s), in a document image. The detection method is based on the piece-wise painting algorithm (PPA) and some probability features along with a decision tree. For the logo recognition, a template based recognition approach is proposed to recognize the logo which may present in every detected logo-patch. The proposed logo recognition strategy uses a search space reduction technique to decrease the number of template logo-models needed for the recognition of a logo in a detected logo-patch. The features used for search space reduction are based on the geometric properties of a detected logo-patch. Based on our experimentations on 1290 document images of Tobacco800 dataset, 99.31% of the logos were detected as logo-patches. Among the detected logo-patches 97.90% of logos were fairly recognized. Considering both logo detection and recognition results, 97.22% of the logos in the document images could truly be detected/recognized as the overall performance of the proposed system.

In this paper, a new model-based writer identification scheme using histogram symbolic representation approach is proposed. In the proposed scheme, initially, some pre-processing techniques are employed to enhance image quality and extract text-lines from each handwritten document image. For each extracted text-line, a set of 92 features are computed based on analysis of connected component, enclosed region, lower and upper contours, fractal code, and Curve let. Considering the extracted feature vectors, a histogram is created for each feature of every writer as a histogram-valued symbolic data. This process results in a handwriting style model for each individual that consists of a set of histograms. To evaluate the proposed scheme, two different handwritten datasets written in two different scripts (Kannada as an Indian based script and English) were used. The first dataset contains 228 pages written in Kannada by 57 people. The other one is the dataset used in SigWiComp2013 composed of 330 document pages written in English by 55 individuals. The same criteria used in the SigWiComp2013 were followed in our evaluation strategy. Concerning the Kannada dataset, an F-measure of 92.79% was obtained when 114 documents were used in learning stage and the rest (114) were used for testing. For the SigWiComp2013 dataset an F-measure of 26.67% was obtained that is fairly comparable to the best result reported in the literature.

In this paper, a coarse-to-fine logo detection scheme for document images is proposed. At the coarse level of the proposed scheme, content of a document image is pruned utilizing a decision tree and a small number of features such as frequency probability (FP), Gaussian probability (GP), height, width, and average density computed for patches. The patches are extracted employing the piece-wise painting algorithm (PPA) used for text-line segmentation. The fine level of the proposed scheme refines the detection results by integrating shape context descriptors and a Nearest Neighbor (NN) classifier. We evaluated the proposed approach using a public and two large industrial datasets. From the experiment on Tobacco-800 dataset, the best precision and accuracy of 75.25% and 91.50% were obtained respectively.

This paper presents the results of the Handwriting Segmentation Contest that was organized in the context of the ICDAR2013. The general objective of the contest was to use well established evaluation practices and procedures to record recent advances in off-line handwriting segmentation. Two benchmarking datasets, one for text line and one for word segmentation, were created in order to test and compare all submitted algorithms as well as some state-of-the-art methods for handwritten document image segmentation in realistic circumstances. Handwritten document images were produced by many writers in two Latin based languages (English and Greek) and in one Indian language (Bangla, the second most popular language in India). These images were manually annotated in order to produce the ground truth which corresponds to the correct text line and word segmentation results. The datasets of previously organized contests (ICDAR2007, ICDAR2009 and ICFHR2010 Handwriting Segmentation Contests) along with a dataset of Bangla document images were used as training dataset. Eleven methods are submitted in this competition. A brief description of the submitted algorithms, the evaluation criteria and the segmentation results obtained from the submitted methods are also provided in this manuscript.

Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal verification. The objective of this paper is to present an empirical contribution towards the understanding of a threshold-based signature verification technique involving off-line Bangla (Bengali) signatures. Experiments on signature verification involving non-English signatures are an important consideration in the signature verification area. Only very few research works employing signatures of Indian script have been considered in the field of non-English signature verification. To fill this gap, a threshold-based scheme for verification considering off-line Bangla signatures is proposed. Some techniques such as under-sampled bitmap, intersection/endpoint and directional chain code are employed for feature extraction. The Nearest Neighbour method is considered for classification. Furthermore, a Bangla signature database, which consists of 2400 (100×24) genuine signatures and 3000 (100×30) forgeries has been created and is employed for experimentation. We obtained a 15.57% Average Error Rate (AER) as the best verification result using directional chain code features employed in this research work.

Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal verification. The objective of this paper is to present an empirical contribution toward the understanding of a threshold-based signature verification technique involving off-line Bangla (Bengali) signatures. Experiments on signature verification concerning non-English signatures are an important consideration in the signature verification area. Only very few research works employing signatures of Indian script have been considered in the field of non-English based signature verification. To fill this gap, a threshold-based scheme for the verification of off-line Bangla signatures is proposed. Some techniques such as under-sampled bitmap, intersection/end point and directional chain code are employed for feature extraction. The thresholds are computed based on the similarity measures obtained employing the nearest neighbor classifier. The SVM classifier has also been considered for mainly comparative experimental result generation. Furthermore, a Bangla signature database, which consists of 2400 (100 × 24) genuine signatures and 3000 (100 × 30) forgeries, has been created and is employed for experimentation. An average error rate (AER) of 12.33% was obtained as the best verification result using directional chain code features in this research work. As a comparative study, a different dataset (GPDS-160) has also been considered.

In this paper, we present an empirical contribution towards the understanding of multi-script signature identification. In the proposed signature identification system, the signatures of Bengali (Bangla), Hindi (Devanagari) and English are considered for the identification process. This system will identify whether a claimed signature belongs to the group of Bengali, Hindi or English signatures. Zernike Moment and histogram of gradient are employed as two different feature extraction techniques. In the proposed system, Support Vector Machines (SVMs) are considered as classifiers for signature identification. A database of 2100 Bangla signatures, 2100 Hindi signatures and 2100 English signatures are used for experimentation. Two different results based on two different feature sets are calculated and analysed. The highest accuracy of 92.14% is obtained based on the gradient features using 4200 (1400 Bangla +1400 Hindi + 1400 English) samples for training and 2100 (700 Bangla +700 Hindi +700 English) samples for testing.

In recent years, many techniques for the recognition of Persian/Arabic handwritten documents have been proposed by researchers. To test the promises of different features extraction and classification methods and to provide a new benchmark for future research, in this paper a comparative study of Persian/Arabic handwritten character recognition using different feature sets and classifiers is presented. Feature sets used in this study are computed based on gradient, directional chain code, shadow, under-sampled bitmap, intersection/junction/endpoint, and line-fitting information. Support Vector Machines (SVMs), Nearest Neighbour (NN), k-Nearest Neighbour (k-NN) are used as different classifiers. We evaluated the proposed systems on a standard dataset of Persian handwritten characters. Using 36682 samples for training, we tested the proposed recognition systems on other 15338 samples and their detailed results are reported. The best correct recognition of 96.91% is obtained in this comparative study.

In document image analysis (DIA) especially in handwritten document recognition, standard databases play significant roles for evaluating performances of algorithms and comparing results obtained by different groups of researchers. The field of DIA regard to Indo-Persian documents is still at its infancy compared to Latin script-based documents; as such standard datasets are not still available in literature. This paper is an effort towards alleviating this gap. In this paper, an unconstrained handwritten dataset containing documents of Persian, Bangla, Oriya and Kannada (PBOK) is introduced. The PBOK contains 707 text-pages written in four different languages (Persian, Bangla, Oriya and Kannada) by 436 individuals. Total number of text-lines, words/subwords and characters are 12,565, 104,541 and 423,980, respectively. In most documents of PBOK dataset contain either an overlapping or a touching text-lines. The average number of text-lines in text-pages of the PBOK dataset is 18. Two types of ground truths, based on pixels information and content information, are generated for the dataset. Because of such ground truths, the PBOK dataset can be utilized in many areas of document image processing e.g. text-line segmentation, word segmentation and word recognition. To provide an insight for other researches, recent text-line segmentation results on this dataset are also reported.

Research towards Indian handwritten document analysis achieved increasing attention in recent years. In pattern recognition and especially in handwritten document recognition, standard databases play vital roles for evaluating performances of algorithms and comparing results obtained by different groups of researchers. For Indian languages, there is a lack of standard database of handwritten texts to evaluate performance of different document recognition approaches and for comparison purpose. In this paper, an unconstrained Kannada handwritten text database (KHTD) is introduced. The KHTD contains 204 handwritten documents of four different categories written by 51 native speakers of Kannada. Total number of text-lines and words in the dataset are 4298 and 26115, respectively. In most of text-pages of the KHTD contains either an overlapping or a touching text-lines and the average number of text-lines in each document on the database is 21. Two types of ground truths based on pixels information and content information are generated for the database. Providing these two types of ground truths for the KHTD, it can be utilized in many areas of document image processing such as sentence recognition/understanding, text-line segmentation, word segmentation, word recognition, and character segmentation. To provide a framework for other researches, recent text-line segmentation results on this dataset are also reported. The KHTD is available for research purposes.

Because of writing styles of different individuals, some of the text-lines may be curved in shape. For recognition of such text-lines, their proper alignment is necessary. In this paper, we propose a text-line alignment technique based on painting algorithm. Here at first, Piece-wise Painting Algorithm (PPA) is used to get a number of black and white rectangular patches all along the text-line for text-line alignment. Identifying the degree of oscillation of the input text-line, some candidate pixels are also obtained based on horizontal projection and center points of the black patches. Using the degree of oscillation of the input text image and the candidate pixels a curve or straight line is fit to trace the baseline. Subsequently, all components of the text-line are deskewed based on analyzing the characteristic of the fit curve or line to align the components with respect to the horizontal imaginary baseline. The proposed algorithm was evaluated with 128 Persian handwritten text-lines containing 4317 sub words. Experimental analysis showed that 92.31% of the sub words were accurately aligned. Further, the proposed algorithm was tested with another Persian handwritten text-lines dataset [6] and remarkable results were achieved.

In this paper, we propose an efficient skew estimation technique based on Piece-wise Painting Algorithm (PPA) for scanned documents. Here we, at first, employ the PPA on the document image horizontally and vertically. Applying the PPA on both the directions, two painted images (one for horizontally painted and other for vertically painted) are obtained. Next, based on statistical analysis some regions with specific height (width) from horizontally (vertically) painted images are selected and top (left), middle (middle) and bottom (right) points of such selected regions are categorized in 6 separate lists. Utilizing linear regression, a few lines are drawn using the lists of points. A new majority voting approach is also proposed to find the best-fit line amongst all the lines. The skew angle of the document image is estimated from the slope of the best-fit line. The proposed technique was tested extensively on a dataset containing various categories of documents. Experimental results showed that the proposed technique achieved more accurate results than the state-of-the-art methodologies.

In this paper, a two-stage scheme for the recognition of Persian handwritten isolated characters is proposed. In the first stage, similar shaped characters are categorized into groups and as a result, 8 groups are obtained from 32 Persian basic characters. In the second stage, the groups containing more than one similar shape characters are considered further for the final recognition. Feature extraction is based on under sampled bitmaps technique and modified chain-code direction frequencies. For the first stage features, we compute 49-dimension features based on under sampled bitmaps from 49 non-overlapping 7 × 7 window-maps. 196-dimension chain-code direction frequencies from 49 overlapping 9 × 9 window-maps are computed and used as features for the second stage of the proposed scheme. Classifiers are one-against-other support vector machines (SVM). We evaluated our scheme on a standard dataset of Persian handwritten characters. Using 36682 samples for training, we tested our scheme on other 15338 samples and obtained 98.10% and 96.68% correct recognition rates when considered 8-class and 32-class problems, respectively.

Most of the countries use bi-script documents. This is because every country uses its own national language and English as second/foreign language. Therefore, bi-lingual document with one language being the English and other being the national language is very common. Postal documents are a very good example of such bi-lingual/script document. This paper deals with word-wise handwritten script identification from bi-script documents written in Persian and Roman. In the proposed scheme, simple but fast computable set of 12 features based on fractal dimension, position of small component, topology etc. are used and a set of classifiers are employed for script identification experiments. We tested our scheme on a dataset of 5000 handwritten Persian and English words and 99.20% of correct script identification is obtained.

In this paper, an efficient approach to segment Persian off-line handwritten text-line into characters is presented. The proposed algorithm first traces the baseline of the input text-line image and straightens it. Subsequently, it over-segments each word/subwords using features extracted from histogram analysis and then removes extra segmentation points using some baseline dependent as well as language dependent rules. We tested the proposed character segmentation scheme with 2 different datasets. On a test set of 899 Persian words/subwords created by us, 90.26% of the characters were segmented correctly. From another dataset of 200 handwritten Arabic word images we obtained 93.49% correct segmentation accuracy.

In this paper, we propose two types of feature sets based on modified chain-code direction frequencies in the contour pixels of input image and modified transition features (horizontally and vertically). A multi-level support vector machine (SVM) is proposed as classifier to recognize Persian isolated digits. In first level, we combine similar shaped numerals into a single group and as result; we obtain 7 classes instead of 10 classes. We compute 196-dimension chain-code direction frequencies as features to discriminate 7 classes. In the second level, classes containing more than one numeral because of high resemblance in their shapes are considered. We use modified transition features (horizontally and vertically) for discriminating between two overlapping classes (0 and 1). To separate another overlapping group containing three numerals 2, 3 and 4 we first eliminate common parts of these digits (tail) and then compute chain code features. We employ SVM classifier for the classification and evaluate our scheme on 80,000 handwritten samples of Persian numerals [10]. Using 60,000 samples for training, we tested our scheme on other 20,000 samples and obtained 99.02% accuracy.