Perceptual Recognition of Arabic Literal Amounts

Since humans are the best readers, one of the most promising trends in automatic handwriting recognition is to get inspiration from psychological reading models. The underlying idea is to derive benefits from studies of human reading, in order to build efficient automatic reading systems. In this context, we propose a human reading inspired system for the recognition of Arabic handwritten literalamounts. Our approach is based on the McClelland and Rumelhart's neural model called IAM, which is one of the most referenced psychological reading models. In this article, we have adapted IAM to suit the Arabic writing characteristics, such as the natural existence of sub-words, and the particularities of the considered literal amounts vocabulary. The core of the proposed system is a neural network classifier with local knowledge representation, structured hierarchically into three levels: perceptual structural features, sub-words and words. In contrast to the classical neural networks, localist approach is more appropriate to our problem. Indeed, it introduces a priori knowledge which leads to a precise structure of the network and avoids the black box aspect as well as the learning phase. Our experimental recognition results are interesting and confirm our expectation that adapting human reading models is a promising issue in automatic handwritten word recognition

Perception and production of syllable structure and stress by adult Libyan Arabic speaker acquiring English in the UK

The field of second language (L2) phonology has recently addressed the related phonological acquisition question of to what extent exposure to native speaker L2 input following exposure to non-native accented L2 input, results in c~anges in the leamer's interlanguage phonology (Akita 2001). If such learners do show changes over time, what kind of changes are these in both perception and production? My study is a contribution to interlanguage studies on the acquisition of prosodic structure, and concentrates on the acquisition of English syllable structure and metrical stress by Arabic speaker. In this study the interlanguage phonology of 28 native Arabic speakers from Libya learning English in natural settings (The UK), was investigated. The average age of the participants was 32.5 years. All the subjects started learning English in school at an average age of 16.0 years. The primary source oflanguage input was the classroom, till an average age of25.0 years. The method of collecting data involved three types of test. The first test covered syllable structure in onset and coda with epenthesised forms and included 185 words. The second test covered metrical stress, and included two sub-tests. Test 2A included 28 words, and test 2B included 84 sentences with grammatical and ungrammatical forms of stress. The third test contained three sub-tests. Test 3A included 9 words, test 3B included four pictures, and test 3C included 28 sentences. Tests cover perception of syllable structure and metrical stress as well as production of syllable structure and metrical stress for each learner. In the perception test learners had to listen to a type and chose an answer from a paper in front of them whereas for production tests learners had to read words, sentences, and talk about pictures. Their production output was recorded and transcribed. Results show differences for the perception and production sub-tasks. There is also some parameter resetting and missetting at the level of metrical stress. These results mirror the findings of Archibald (1993) Pater (1997) and Mousa (1994).EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Cognitive Science and the Origin of Lexical Metaphor: A Neurofunctional Shift (NFS) Hypothesis

A long-standing and cardinal issue in the cognitive science and humanities research literatures on lexical metaphor is whether figurative language is derived from literal language. In examining this issue, research from a broad spectrum of studies in both cognitive science and the humanities is addressed with particular attention to findings from classicist research on ancient Greek texts, on the cognitive significance of the invention of the Crreek vocalic alphabet. These findings are related to current research on brain hemispheric laterality. It will be suggested that lexical metaphor was originally not a linguistic figure-of speech derived from literal language but only later came to be so conceptualized as the consequence of a neurofunctional shift (NFS) in hemispheric laterality, a shift that was precipitated in part by the invention and adoption of the Crreek vocalic alphabet. It will be further suggested that the prevailing view of metaphor as a linguistic figure of speech is the consequence of an inappropriate cognitive turn that resulted in a superimposition or back scanning of a modern alphabetic-based epistemology on to phenomena originating in a preliterate cultur

Recognizing Visual Object Using Machine Learning Techniques

Nowadays, Visual Object Recognition (VOR) has received growing interest from researchers and it has become a very active area of research due to its vital applications including handwriting recognition, diseases classification, face identification ..etc. However, extracting the relevant features that faithfully describe the image represents the challenge of most existing VOR systems. This thesis is mainly dedicated to the development of two VOR systems, which are presented in two different contributions. As a first contribution, we propose a novel generic feature-independent pyramid multilevel (GFIPML) model for extracting features from images. GFIPML addresses the shortcomings of two existing schemes namely multi-level (ML) and pyramid multi-level (PML), while also taking advantage of their pros. As its name indicates, the proposed model can be used by any kind of the large variety of existing features extraction methods. We applied GFIPML for the task of Arabic literal amount recognition. Indeed, this task is challenging due to the specific characteristics of Arabic handwriting. While most literary works have considered structural features that are sensitive to word deformations, we opt for using Local Phase Quantization (LPQ) and Binarized Statistical Image Feature (BSIF) as Arabic handwriting can be considered as texture. To further enhance the recognition yields, we considered a multimodal system based on the combination of LPQ with multiple BSIF descriptors, each one with a different filter size. As a second contribution, a novel simple yet effcient, and speedy TR-ICANet model for extracting features from unconstrained ear images is proposed. To get rid of unconstrained conditions (e.g., scale and pose variations), we suggested first normalizing all images using CNN. The normalized images are fed then to the TR-ICANet model, which uses ICA to learn filters. A binary hashing and block-wise histogramming are used then to compute the local features. At the final stage of TR-ICANet, we proposed to use an effective normalization method namely Tied Rank normalization in order to eliminate the disparity within blockwise feature vectors. Furthermore, to improve the identification performance of the proposed system, we proposed a softmax average fusing of CNN-based feature extraction approaches with our proposed TR-ICANet at the decision level using SVM classifier

Human Reading Based Strategies for off-line Arabic Word Recognition

International audienceThis paper summarizes some techniques proposed for off-line Arabic word recognition. The point of view developed here concerns the human reading favoring an interactive mechanism between global memorization and local checking making easier the recognition of complex scripts as Arabic. According to this consideration, some specific papers are analyzed and their strategies commente

Language statistics as a window into mental representations

Large-scale linguistic data is nowadays available in abundance. Using this source of data, previous research has identified redundancies between the statistical structure of natural language and properties of the (physical) world we live in. For example, it has been shown that we can gauge city sizes by analyzing their respective word frequencies in corpora. However, since natural language is always produced by human speakers, we point out that such redundancies can only come about indirectly and should necessarily be restricted cases where human representations largely retain characteristics of the physical world. To demonstrate this, we examine the statistical occurrence of words referring to body parts in very different languages, covering nearly 4 billions of native speakers. This is because the convergence between language and physical properties of the stimuli clearly breaks down for the human body (i.e., more relevant and functional body parts are not necessarily larger in size). Our findings indicate that the human body as extracted from language does not retain its actual physical proportions; instead, it resembles the distorted human-like figure known as the sensory homunculus, whose form depicts the amount of cortical area dedicated to sensorimotor functions of each body part (and, thus, their relative functional relevance). This demonstrates that the surface-level statistical structure of language opens a window into how humans represent the world they live in, rather than into the world itself