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

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

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

Strictly One-Dimensional Electron System in Au Chains on Ge(001) Revealed By Photoelectron K-Space Mapping

Atomic nanowires formed by Au on Ge(001) are scrutinized for the band topology of the conduction electron system by k-resolved photoemission. Two metallic electron pockets are observed. Their Fermi surface sheets form straight lines without undulations perpendicular to the chains within experimental uncertainty. The electrons hence emerge as strictly confined to one dimension. Moreover, the system is stable against a Peierls distortion down to 10 K, lending itself for studies of the spectral function. Indications for unusually low spectral weight at the chemical potential are discussed.Comment: 4 pages, 4 figures - revised version with added Fig. 2e) and additional reference

New Model System for a One-Dimensional Electron Liquid: Self-Organized Atomic Gold Chains on Ge(001)

Unique electronic properties of self-organized Au atom chains on Ge(001) in novel c(8x2) long-range order are revealed by scanning tunneling microscopy. Along the nanowires an exceptionally narrow conduction path exists which is virtually decoupled from the substrate. It is laterally confined to the ultimate limit of single atom dimension, and is strictly separated from its neighbors, as not previously reported. The resulting tunneling conductivity shows a dramatic inhomogeneity of two orders of magnitude. The atom chains thus represent an outstandingly close approach to a one-dimensional electron liquid.Comment: 4 pages, 4 figures, title reworded, references added, accepted in Phys. Rev. Lett. (20 Oct 2008

Structural Examination of Au/Ge(001) by Surface X-Ray Diffraction and Scanning Tunneling Microscopy

The one-dimensional reconstruction of Au/Ge(001) was investigated by means of autocorrelation functions from surface x-ray diffraction (SXRD) and scanning tunneling microscopy (STM). Interatomic distances found in the SXRD-Patterson map are substantiated by results from STM. The Au coverage, recently determined to be 3/4 of a monolayer of gold, together with SXRD leads to three non-equivalent positions for Au within the c(8x2) unit cell. Combined with structural information from STM topography and line profiling, two building blocks are identified: Au-Ge hetero-dimers within the top wire architecture and Au homo-dimers within the trenches. The incorporation of both components is discussed using density functional theory and model based Patterson maps by substituting Germanium atoms of the reconstructed Ge(001) surface.Comment: 5 pages, 3 figure

Robust Sparse Learning Based on Kernel Non-Second Order Minimization

© 2019 IEEE. Partial occlusions in face images pose a great problem for most face recognition algorithms due to the fact that most of these algorithms mainly focus on solving a second order loss function, e.g., mean square error (MSE), which will magnify the effect from occlusion parts. In this paper, we proposed a kernel non-second order loss function for sparse representation (KNS-SR) to recognize or restore partially occluded facial images, which both take the advantages of the correntropy and the non-second order statistics measurement. The resulted framework is more accurate than the MSE-based ones in locating and eliminating outliers information. Experimental results from image reconstruction and recognition tasks on publicly available databases show that the proposed method achieves better performances compared with existing methods

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