A study on idiosyncratic handwriting with impact on writer identification

© 2018 IEEE. In this paper, we study handwriting idiosyncrasy in terms of its structural eccentricity. In this study, our approach is to find idiosyncratic handwritten text components and model the idiosyncrasy analysis task as a machine learning problem supervised by human cognition. We employ the Inception network for this purpose. The experiments are performed on two publicly available databases and an in-house database of Bengali offline handwritten samples. On these samples, subjective opinion scores of handwriting idiosyncrasy are collected from handwriting experts. We have analyzed the handwriting idiosyncrasy on this corpus which comprises the perceptive ground-truth opinion. We also investigate the effect of idiosyncratic text on writer identification by using the SqueezeNet. The performance of our system is promising

Cognitive Analysis for Reading and Writing of Bengali Conjuncts

© 2018 IEEE. In this paper, we study the difficulties arising in reading and writing of Bengali conjunct characters by human-beings. Such difficulties appear when the human cognitive system faces certain obstructions in effortlessly reading/writing. In our computer-based investigation, we consider the reading/writing difficulty analysis task as a machine learning problem supervised by human perception. To this end, we employ two distinct models: (a) an auto-derived feature-based Inception network and (b) a hand-crafted feature-based SVM (Support Vector Machine). Two commonly used Bengali printed fonts and three contemporary handwritten databases are used for collecting subjective opinion scores from human readers/writers. On this corpus, which contains the perceptive ground-truth opinion of reading/writing complications, we have undertaken to conduct the experiments. The experimental results obtained on various types of conjunct characters are promising

An empirical study on writer identification and verification from intra-variable individual handwriting

© 2013 IEEE. The handwriting of a person may vary substantially with factors, such as mood, time, space, writing speed, writing medium/tool, writing a topic, and so on. It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of an individual, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person. However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting. In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing. Here, we work on writer identification/verification from highly intra-variable offline Bengali writing. To this end, we use various models mainly based on handcrafted features with support vector machine and features auto-derived by the convolutional network. For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique. We have obtained some interesting results

Offline Bengali writer verification by PDF-CNN and siamese net

© 2018 IEEE. Automated handwriting analysis is a popular area of research owing to the variation of writing patterns. In this research area, writer verification is one of the most challenging branches, having direct impact on biometrics and forensics. In this paper, we deal with offline writer verification on complex handwriting patterns. Therefore, we choose a relatively complex script, i.e., Indic Abugida script Bengali (or, Bangla) containing more than 250 compound characters. From a handwritten sample, the probability distribution functions (PDFs) of some handcrafted features are obtained and input to a convolutional neural network (CNN). For such a CNN architecture, we coin the term 'PDFCNN', where handcrafted feature PDFs are hybridized with auto-derived CNN features. Such hybrid features are then fed into a Siamese neural network for writer verification. The experiments are performed on a Bengali offline handwritten dataset of 100 writers. Our system achieves encouraging results, which sometimes exceed the results of state-of-The-Art techniques on writer verification

Text-line-up: Don’t Worry About the Caret

In a freestyle handwritten text-line, sometimes words are inserted using a caret symbol (∧ ) for corrections/annotations. Such insertions create fluctuations in the reading sequence of words. In this paper, we aim to line-up the words of a text-line, so that it can assist the OCR engine. Previous text-line segmentation techniques in the literature have scarcely addressed this issue. Here, the task undertaken is formulated as a path planning problem, and a novel multi-agent hierarchical reinforcement learning-based architecture solution is proposed. As a matter of fact, no linguistic knowledge is used here. Experimentation of the proposed solution architecture has been conducted on English and Bengali offline handwriting, which yielded some interesting results

Overexpression of the duffy antigen receptor for chemokines (DARC) by NSCLC tumor cells results in increased tumor necrosis

BACKGROUND: The Duffy antigen receptor for chemokines (DARC) is known to be a promiscuous chemokine receptor that binds a variety of CXC and CC chemokines in the absence of any detectable signal transduction events. Within the CXC group of chemokines, DARC binds the angiogenic CXC chemokines including IL-8 (CXCL8), GROα (CXCL1) and ENA-78 (CXCL5), all of which have previously been shown to be important in non-small cell lung carcinoma (NSCLC) tumor growth. We hypothesized that overexpression of DARC by a NSCLC tumor cell line would result in the binding of the angiogenic ELR+ CXC chemokines by the tumor cells themselves, and thus interfere with the stimulation of endothelial cells and induction of angiogenesis by the tumor cell-derived angiogenic chemokines. RESULTS: NSCLC tumor cells that constitutively expressed DARC were generated and their growth characteristics were compared to control transfected cells in vitro and in vivo in SCID animals. We found that tumors derived from DARC-expressing cells were significantly larger in size than tumors derived from control-transfected cells. However, upon histological examination we found that DARC-expressing tumors had significantly more necrosis and decreased tumor cellularity, as compared to control tumors. Expression of DARC by NSCLC cells was also associated with a decrease in tumor-associated vasculature and a reduction in metastatic potential. CONCLUSIONS: The expression of DARC in the context of NSCLC tumors may act as a chemokine decoy receptor and interferes with normal tumor growth and chemokine-induced tumor neovascularization

Improving the Performance of Thinning Algorithms with Directed Rooted Acyclic Graphs

In this paper we propose a strategy to optimize the performance of thinning algorithms. This solution is obtained by combining three proven strategies for binary images neighborhood exploration, namely modeling the problem with an optimal decision tree, reusing pixels from the previous step of the algorithm, and reducing the code footprint by means of Directed Rooted Acyclic Graphs. A complete and open-source benchmarking suite is also provided. Experimental results confirm that the proposed algorithms clearly outperform classical implementations

Higuchi Dimension of Digital Images

There exist several methods for calculating the fractal dimension of objects represented as 2D digital images. For example, Box counting, Minkowski dilation or Fourier analysis can be employed. However, there appear to be some limitations. It is not possible to calculate only the fractal dimension of an irregular region of interest in an image or to perform the calculations in a particular direction along a line on an arbitrary angle through the image. The calculations must be made for the whole image. In this paper, a new method to overcome these limitations is proposed. 2D images are appropriately prepared in order to apply 1D signal analyses, originally developed to investigate nonlinear time series. The Higuchi dimension of these 1D signals is calculated using Higuchi's algorithm, and it is shown that both regions of interests and directional dependencies can be evaluated independently of the whole picture. A thorough validation of the proposed technique and a comparison of the new method to the Fourier dimension, a common two dimensional method for digital images, are given. The main result is that Higuchi's algorithm allows a direction dependent as well as direction independent analysis. Actual values for the fractal dimensions are reliable and an effective treatment of regions of interests is possible. Moreover, the proposed method is not restricted to Higuchi's algorithm, as any 1D method of analysis, can be applied

Three Dimensional Visualization and Fractal Analysis of Mosaic Patches in Rat Chimeras: Cell Assortment in Liver, Adrenal Cortex and Cornea

The production of organ parenchyma in a rapid and reproducible manner is critical to normal development. In chimeras produced by the combination of genetically distinguishable tissues, mosaic patterns of cells derived from the combined genotypes can be visualized. These patterns comprise patches of contiguously similar genotypes and are different in different organs but similar in a given organ from individual to individual. Thus, the processes that produce the patterns are regulated and conserved. We have previously established that mosaic patches in multiple tissues are fractal, consistent with an iterative, recursive growth model with simple stereotypical division rules. Fractal dimensions of various tissues are consistent with algorithmic models in which changing a single variable (e.g. daughter cell placement after division) switches the mosaic pattern from islands to stripes of cells. Here we show that the spiral pattern previously observed in mouse cornea can also be visualized in rat chimeras. While it is generally held that the pattern is induced by stem cell division dynamics, there is an unexplained discrepancy in the speed of cellular migration and the emergence of the pattern. We demonstrate in chimeric rat corneas both island and striped patterns exist depending on the age of the animal. The patches that comprise the pattern are fractal, and the fractal dimension changes with the age of the animal and indicates the constraint in patch complexity as the spiral pattern emerges. The spiral patterns are consistent with a loxodrome. Such data are likely to be relevant to growth and cell division in organ systems and will help in understanding how organ parenchyma are generated and maintained from multipotent stem cell populations located in specific topographical locations within the organ. Ultimately, understanding algorithmic growth is likely to be essential in achieving organ regeneration in vivo or in vitro from stem cell populations

Topoisomerase II\u3b2 mediates the resistance of glioblastoma stem cells to replication stress-inducing drugs

The mesenchymal state in cancer is usually associated with poor prognosis due to the metastatic predisposition and the hyper-activated metabolism. Exploiting cell glucose metabolism we propose a new method to detect mesenchymal-like cancer cells. We demonstrate that the uptake of glucose-coated magnetic nanoparticles (MNPs) by mesenchymal-like cells remains constant when the glucose in the medium is increased from low (5.5 mM) to high (25 mM) concentration, while the MNPs uptake by epithelial-like cells is significantly reduced. These findings reveal that the glucose-shell of MNPs plays a major role in recognition of cells with high-metabolic activity. By selectively blocking the glucose transporter 1 channels we showed its involvement in the internalization process of glucose-coated MNPs. Our results suggest that glucose-coated MNPs can be used for metabolic-based assays aimed at detecting cancer cells and that can be used to selectively target cancer cells taking advantage, for instance, of the magnetic-thermotherapy