Signature Verification Approach using Fusion of Hybrid Texture Features

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio

Non-english and non-latin signature verification systems: A survey

Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a large number of documents is a difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable unique physical characteristics (e.g. hand geometry, iris scan, finger prints or DNA) or behavioural features. Substantial research has been undertaken in the field of signature verification involving English signatures, but to the best of our knowledge, very few works have considered non-English signatures such as Chinese, Japanese, Arabic etc. In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of non-English and non-Latin signature verification systems

Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Consequences of bi-literacy in bilingual individuals: in the healthy and neurologically impaired

Background. In the current global, cross-cultural scenario, being bilingual or multilingual is a norm rather than an exception. In such an environment an individual may be actively involved in reading and writing in all their languages in addition to speaking them. Regular use of two or more languages is termed as bilingualism and being able to read and write in both of them is referred to as bi-literacy. Research indicates that bilingualism has an impact on language production and cognition, specifically executive functions. Given the impact of literacy and bilingualism, the reasonable question that arises, is whether bi-literacy would offer an additional impact on language production and cognition. This becomes even more relevant in a multilingual, multi-cultural society such as India. We examined the impact of bi-literacy on oral language production (at word and connected speech level), comprehension and on non-verbal executive function measures in bi-literate bilingual healthy adults in an immigrant diaspora living in the UK. In addition to English, they were speakers of one of the South Indian languages (Kannada, Malayalam, Tamil and Telugu). The significance of bi-literacy among bilinguals assumes further importance in aphasia (language impairment due to brain damage). For those who have aphasia in one or more languages due to brain damage, the severity of impairment maybe different in both languages, also the modalities of language may be differentially affected. In particular, reading and writing maybe impaired differently in the languages used by a bi/multilingual. Manifestation of reading impairments are also dependent on the nature of the script of the language being read [e.g., Raman & Weekes (2005) report differential dyslexia in a Turkish-English speaker who exhibited surface dyslexia in English and deep dysgraphia in Turkish]. Our study contributes to the field of bilingual aphasia by focusing specifically on reading differing from the existing literature of aphasia in bilinguals, where the focus has predominantly been on language production and comprehension. Studying reading impairments provides a better understanding of how the reading impairments are manifested in the two languages, which will aid appropriate assessment and intervention. This research investigated the impact of bi-literacy in both populations (healthy adults and neurologically impaired) in two phases: Phase I (in UK) and Phase II (in India). Aim. Phase I investigated the impact of bi-literacy on oral language production (at word level and connected speech), comprehension and non-verbal executive function in bi-literate bilingual healthy adults. Phase II examined the reading impairments in two languages of bilingual persons with aphasia (BPWA). Methods. For Phase I, participants were thirty-four bi-literate bilingual healthy adults with English as their L2 and one of the Dravidian languages (Kannada, Malayalam, Tamil and Telugu) as their L1. We have used the term ‘print exposure’ as a proxy for literacy. They were divided into a high print exposure (HPE, n=22) and a low print exposure (LPE, n=12) group based on their performance on two tasks measuring L2 print exposure- grammaticality judgement task and sentence verification task. We also quantified their bilingual characteristics- proficiency, reading and writing characteristics and dominance. The groups were matched on years of education, age and gender. Participants completed a set of oral language production tasks in L2 (at word level) namely -verbal fluency, word and non-word repetition; comprehension tasks in L2 namely synonymy triplets task and sentence comprehension task (Chapter 2); oral narrative task in L2 (at connected speech level) (Chapter 3) followed by non-verbal executive function tasks tapping into inhibitory control (Spatial Stroop and Flanker tasks), working memory (visual n-back and auditory n-back) and task switching (colour-shape task) (Chapter 4). For Phase II, we characterized the reading abilities of four BPWA who spoke one of the Dravidian languages (Kannada, Tamil, Telugu) (alpha-syllabic) as their L1 and English (alphabetic) as their L2. We quantified their bilingual characteristics- proficiency, reading and writing characteristics and dominance. Subtests from the Psycholinguistic Assessment of Language Processing in Aphasia (PALPA; Kay, Lesser & Coltheart, 1992) were used to document the reading profile of BPWA in English and reading subtests from Reading Acquisition Profile (RAP-K; Rao, 1997) and words from Bilingual Aphasia test -Hindi (BAT; Paradis & Libben, 1987) were used to document the reading profile of BPWA in Kannada and Hindi respectively. Findings. Based on the findings of Phase I (i.e., results from Chapter 2-4), we found prominent differences between HPE and LPE on comprehension measures (synonymy triplets and sentence comprehension tasks). This is in contrast to the results observed in monolingual adults, were semantics is less impacted by print exposure. Moreover, our predictions that HPE will result in better oral language production skills were borne out in specific conditions-semantic fluency and non-word repetition task (at word level) and higher number of words in the narrative, higher verbs per utterance and fewer repetitions (at connected speech level). In addition, the non-verbal executive functions, we found no direct link between print exposure (in L2) and non-verbal executive functions in bi-literate bilinguals excepting working memory (auditory N-back task). Additionally, another consistency in our findings is that there seems to be a strong link between print exposure and semantic processing in our research. The findings on the semantic tasks have been consistent across comprehension (synonymy triplets task and sentence comprehension task) and production (semantic fluency) favouring HPE. The findings from Phase II (Chapter 5) reveal differences of reading characteristics in the two languages (with different scripts) of the four BPWA. This research provides preliminary evidence that a script related difference exists in the manifestation of dyslexia in bi-scriptal BPWA speaking a combination of alphabetic and alpha-syllabic languages. Conclusions. Our research contributes to the existing literature by highlighting the relationship between bi-literacy and language production, comprehension and non-verbal cognition where bi-literacy seems to have a higher impact on language than cognition. The contrary findings from the monolinguals and children literature, highlight the importance for considering nuances of bilingual research and specifically challenges the notion that semantic comprehension is not significantly affected by literacy. In the neurologically impaired population, our research provides a comprehensive profiling of reading abilities in BPWA in the Indian population with languages having different scripts. Using this profiling and classification, we are able to affirm the findings previously found in literature emphasizing the importance of script in the assessment of reading abilities in BPWA. Such profiling and classification assist in the development of bilingual models of reading aloud and classifying different types of reading impairments

Continuous Space Models for CLIR

[EN] We present and evaluate a novel technique for learning cross-lingual continuous space models to aid cross-language information retrieval (CLIR). Our model, which is referred to as external-data composition neural network (XCNN), is based on a composition function that is implemented on top of a deep neural network that provides a distributed learning framework. Different from most existing models, which rely only on available parallel data for training, our learning framework provides a natural way to exploit monolingual data and its associated relevance metadata for learning continuous space representations of language. Cross-language extensions of the obtained models can then be trained by using a small set of parallel data. This property is very helpful for resource-poor languages, therefore, we carry out experiments on the English-Hindi language pair. On the conducted comparative evaluation, the proposed model is shown to outperform state-of-the-art continuous space models with statistically significant margin on two different tasks: parallel sentence retrieval and ad-hoc retrieval.We thank German Sanchis Trilles for helping in conducting experiments with machine translation. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce Titan GPU used for this research. The research of the first author was supported by FPI grant of UPV. The research of the third author is supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAMATER (PrometeolI/2014/030).Gupta, P.; Banchs, R.; Rosso, P. (2017). Continuous Space Models for CLIR. Information Processing & Management. 53(2):359-370. https://doi.org/10.1016/j.ipm.2016.11.002S35937053