Reading Recognition Research Articles

Reading Amount and Reading Strategy as Mediators of the Effects of Intrinsic and Extrinsic Reading Motivation on Reading Achievement.

This study was conducted to examine the roles of reading amount and reading strategy as mediators of the effects of intrinsic and extrinsic reading motivation on reading achievement. A sample of 522 7th–9th graders from two public schools in Eastern China participated in the study and completed the questionnaires. The confirmatory factor analyses showed that Curiosity, Involvement, and Challenge as dimensions of intrinsic reading motivation and Recognition, Grades, and Competition as dimensions of extrinsic reading motivation represented reading motivation well in this Chinese sample population. Structural equation modeling analyses showed that intrinsic reading motivation had a positive direct effect on reading achievement, whereas extrinsic reading motivation exerted a negative direct effect on reading achievement. Both intrinsic and extrinsic reading motivation positively predicted reading strategy; however, only intrinsic reading motivation was positively correlated with reading amount. Neither reading amount nor reading strategy mediated the effects of intrinsic and extrinsic reading motivation on reading achievement. The implications of these findings for literacy research and instruction are discussed.

Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition

Structural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.

Combining fixation and lateral masking training enhances perceptual learning effects in patients with macular degeneration.

Macular degeneration (MD), a retinal disease affecting central vision, represents the leading cause of visual impairment in the Western world, and MD patients face severe limitations in daily activities like reading and face recognition. A common compensation strategy adopted by these patients involves the use of a region in the spared peripheral retina as a new fixation spot and oculomotor reference (preferred retinal locus, or PRL). Still, peripheral vision is characterized by poorer visual acuity, fixation stability, and larger crowding zones that further hinder processes like object recognition, visual search, and reading. Perceptual learning (PL) has been successfully used to improve visual acuity in mild visual conditions (e.g., presbyopia, amblyopia and myopia), but results in MD are less clear, often showing limited generalization of learning, unlike what is observed in a healthy population. A possible reason is the suboptimal fixation in the PRL that might prevent patients from processing the briefly presented training stimuli. Following this hypothesis, we trained five MD patients and four age- and eccentricity-matched controls with a protocol that combined contrast detection and a task previously used to train fixation stability. Results showed transfer of learning to crowding reduction, reading speed, and visual acuity in both MD patients and controls. These results suggest that in the case of central vision loss, PL training might benefit from the integration of oculomotor components to optimize the effect of training and promote transfer of learning to other visual functions.

A-226 Identifying Performance Validity Indices within the NIH Toolbox

Abstract Objective The National Institute of Health Toolbox–Cognitive Battery (NIHTB-CB) is a rigorously developed, state-of-the-art neuropsychological screening battery. However, one of its current limitations is the lack of any embedded measures of performance validity. To address this critical gap, the current project explored the relevant literature to determine which NIHTB-CB indices might be most suitable for further investigation. Data Selection A targeted literature review was conducted to explore the development of embedded indicators of performance validity within contemporary neuropsychological batteries. The most foundational and scientifically rigorous literature was gathered using targeted search terms in PsycInfo and Google Scholar. Data Synthesis This selected literature base was reviewed and compared to relevant NIHTB-CB indices. The most suitable indices within the NIHTB-CB appear to include: (1) a pattern of responding on the recognition trial for the Picture Sequence Memory Test that is inconsistent with that of individuals with known brain damage; (2) a severely impaired score on the Picture Vocabulary Test and the Oral Reading Recognition Test; (3) the frequency and length of unusual reaction time differences on the Pattern Comparison Processing Speed Test; and (4) overall performance comparisons with groups with known brain injury or illness. Conclusions Several indices within the NIHTB-CB hold great promise as performance validity indicators. Future research is needed to establish the validity of these indices using simulation and known-group designs. Establishing indicators of performance validity within the NIHTB-CB will result in more rigorous clinical research and improved clinical practice.

Sensitivity to meaningful regularities acquired through experience

English spelling provides multiple cues to word meaning, and these cues are readily exploited by skilled readers. In two crowdsourcing studies, we tested skilled readers’ sensitivity to a large number of morphological as well as nonmorphological orthographic cues by asking them to classify nonwords as adjectives or nouns. We observed a substantial variability across individuals and orthographic cues. In this paper, we discuss some sources of this variation. Specifically, we found consistent correlations between readers’ sensitivity to cues and their performance on language tasks (reading, spelling, and author recognition tests) suggesting that reading experience is critical for assimilating spelling-to-meaning regularity from written language. Further, we identified characteristics that may be important for the learning and exploitation of orthographic cues that are related to the nature of their function and use in context.

Method on water level ruler reading recognition based on image processing

Water level measure is the key task in hydrological monitoring, wherein water level ruler (WLR) is the necessary enabled measure instrument. Due to its low cost and easy deployment, WLR has been widely used for flood monitoring in many hydrological stations. For saving labor costs, it is very desirable to achieve automatic reading of WLR, which has recently become a focus of the research in hydrological field. However, in automatic reading of WLR, there exist several issues or challenges, which mainly include low recognition rate of characters on WLR and low precision in calculating water level. To address these issues, in this paper we propose a practical and efficient method which is based on image processing. The proposed method consists of three important components, i.e., (1) a multi-template matching algorithm to recognize the characters on WLR, (2) a sequence verification algorithm to check and refine the recognized characters, and (3) a projection height comparison method to achieve accurate reading even under the circumstance of incomplete characters. We conduct experiments on real-world data to verify the efficiency of our proposed method. The experiment results show that the proposed method achieves $$63\%$$ recognition rate of characters on WLR, as well as average measure error of $$\pm {0.90\hbox { cm}}$$ which is much smaller than the national error standards on water-level monitoring in China ($$\pm {1.0\hbox { cm}}$$). Therefore, we believe our proposed method could be useful for facilitating effective water level measure in practice.

Network connectivity predicts language processing in healthy adults.

Brain imaging has been used to predict language skills during development and neuropathology but its accuracy in predicting language performance in healthy adults has been poorly investigated. To address this shortcoming, we studied the ability to predict reading accuracy and single‐word comprehension scores from rest‐ and task‐based functional magnetic resonance imaging (fMRI) datasets of 424 healthy adults. Using connectome‐based predictive modeling, we identified functional brain networks with >400 edges that predicted language scores and were reproducible in independent data sets. To simplify these complex models we identified the overlapping edges derived from the three task‐fMRI sessions (language, working memory, and motor tasks), and found 12 edges for reading recognition and 11 edges for vocabulary comprehension that accounted for 20% of the variance of these scores, both in the training sample and in the independent sample. The overlapping edges predominantly emanated from language areas within the frontoparietal and default‐mode networks, with a strong precuneus prominence. These findings identify a small subset of edges that accounted for a significant fraction of the variance in language performance that might serve as neuromarkers for neuromodulation interventions to improve language performance or for presurgical planning to minimize language impairments.

Predicting reading ability from brain anatomy and function: From areas to connections

Reading is a complex task involving different brain areas. As a crystallized ability, reading is also known to have effects on brain structure and function development. However, there are still open questions about what are the elements of the reading networks and how structural and functional brain measures shape the reading ability. The present study used a data-driven approach to investigate whether reading-related brain structural measures of cortical thickness, myelination, sulcus depth and structural connectivity and functional connectivity from the whole brain can predict individual differences in reading skills. It used different brain measures and performance scores from the Oral Reading Recognition Test (ORRT) measuring reading ability from 998 participants. We revealed reading-related brain areas and connections, and evaluated how well area and connection measures predict reading performance. Interestingly, the combination of all brain measures obtained the best predictions. We further grouped reading-related areas into positive and negative networks, each with four different levels (Core Regions, Extended-Regions 1, 2, 3), representing different correlation levels with the reading scores, and the non-correlated Region irrelevant to reading ability. The Core Regions are composed of areas that are most strongly correlated with reading performance. Insular and frontal opercular cortex, lateral temporal cortex, and early auditory cortex occupy the positive Core Region, while inferior temporal and motor cortex occupy the negative Core Region. Aside from those areas, the present study also found more reading-related areas including visual and language-related areas. In addition, connections predicting reading scores are denser inside the reading-related networks than outside. Together, the present study reveals extended reading networks of the brain and provides an extended data-driven analytical framework to study interpretable brain-behavior relationships, which are transferable also to studying other abilities.

Problems with inattention, reading comprehension, and autonomic regulation on the Stroop task

AbstractDirect and moderated connections among symptoms of inattention, High‐Frequency Heart Rate Variability (HF‐HRV), and reading comprehension scores were examined. Children age 7 to 12 were rated by both parents and teachers on symptoms of inattention with the Behavior Assessment System for Children. Reading was assessed with the Peabody Individualized Achievement Test, and HF‐HRV was examined during a Stroop task. Controlling for reading recognition scores, results were consistent with previous work, showing HF‐HRV was significantly negatively associated with reading comprehension, with lower HF‐HRV predicting higher reading scores. HF‐HRV also significantly moderated the relationship between symptoms of inattention and reading comprehension, suggesting that as symptoms of inattention increase, children with low HF‐HRV had higher reading comprehension scores. Variations in parasympathetic reactivity may help explain reading comprehension heterogeneity for children with symptoms of inattention.

Cognitive Reading and Character Recognition in Image Processing Techniques

The trend of researches in cognitive reading has become so popular in programming area in the field of computer science from late 1990s when scientists and researchers show more interests in computational approaches are complex in nature to derive from a known algorithm of solution. For instance, in the research areas of biology, medicine and human management sciences there are various problems where we need cognitive reading to deliver a complex and in-exact solution when there is no polynomial time to arrive at an exact solution. This article explains some of the methods in cognitive reading in image processing for character recognition and briefly discusses the steps involved in the process of character recognition in image processing.

Self-Learning Based Cognitive Reading and Character Recognition in Image Processing Techniques

There are many techniques involved in Handwritten character recognition and many of the methods uses common process like pre-processing, segmentation, stroke identification and character interpretation. But the technique applied for these steps differ from different implementation. Building the training data is a tedious task but it is more important as the success of entire recognition system lies on the amount of training the neural network and building the knowledge base. Hence this is an important task and it is costly due to amount of time and resource used for training the dataset. This article explains some of the methods in Cognitive reading in image processing for character recognition and introduces a self-learning based training system which provides improved method in less time/resource consuming in automatically training the knowledge base.

Flexible Micropillar Array for Pressure Sensing in High Density Using Image Sensor

AbstractA stable flexible pressure array sensor is a key point for the development of smart robotics and prosthetic solutions. Traditional flexible pressure sensors are mainly based on piezoresistive, capacitive, and piezoelectric effect. However, pressure array sensors based on these principles need complicated wire links and complex fabrication processes. In addition, sensors based on capacitors are susceptible to interference, while piezoresistive sensors have drift problems. In this paper, a vision‐based flexible device integrating a transparent substrate with a black micropillar array is proposed. An image sensor is introduced to measure pillar sectional‐area variation caused by external pressure. Low‐cost precision machining is used to make the mold which avoids complex fabrication process in terms of lithography process. Fabricating micropillars and substrate respectively with Ecoflex gel and polydimethylsiloxane (PDMS) implies adhesion of two polymer materials. The sensor demonstrates a considerable pressure sensitivity of 0.133 kPa−1 in the pressure range 0–3 kPa owing to the softness of Ecoflex gel. Application in high‐density pressure distribution measurement such as braille reading and shape recognition is presented. The device is also believed to have promising potential applications in wearable devices, for example, arterial pulse signal measurements.

The NIH Toolbox: Overview of Development for Use with Hispanic Populations.

Hispanics/Latinos are the largest and fastest-growing minority population in the United States. To facilitate appropriate outcome assessment of this expanding population, the NIH Toolbox for Assessment of Neurological and Behavioral Function® (NIH Toolbox®) was developed with particular attention paid to the cultural and linguistic needs of English- and Spanish-speaking Hispanics/Latinos. A Cultural Working Group ensured that all included measures were appropriate for use with Hispanics/Latinos in both English and Spanish. In addition, a Spanish Language Working Group assessed all English-language NIH Toolbox measures for translatability. Measures were translated following the Functional Assessment of Chronic Illness Therapy (FACIT) translation methodology for instances where language interpretation could impact scores, or a modified version thereof for more simplified translations. The Spanish versions of the NIH Toolbox Cognition Battery language measures (i.e., Picture Vocabulary Test, Oral Reading Recognition Test) were developed independently of their English counterparts. The Spanish-language version of the NIH Toolbox provides a much-needed set of tools that can be selected as appropriate to complement existing protocols being conducted with the growing Hispanic/Latino population in the United States.

Assessing the Word Recognition Skills of German Elementary Students in Silent Reading—Psychometric Properties of an Item Pool to Generate Curriculum-Based Measurements

Given the high proportion of struggling readers in school and the long-term negative consequences of underachievement for those affected, the question of prevention options arises. The early identification of central indicators for reading literacy is a noteworthy starting point. In this context, curriculum-based measurements have established themselves as reliable and valid instruments for monitoring the progress of learning processes. This article is dedicated to the assessment of word recognition in silent reading as an indicator of adequate reading fluency. The process of developing an item pool is described, from which instruments for learning process diagnostics can be derived. A sample of 4268 students from grades 1–4 processed a subset of items. Each student template included anchor items, which all students processed. Using Item Response Theory, item statistics were estimated for the entire sample and all items. After eliminating unsuitable items (N = 206), a one-dimensional, homogeneous pool of items remained. In addition, there are high correlations with another established reading test. This provides the first evidence that the recording of word recognition skills for silent reading can be seen as an economic indicator for reading skills. Although the item pool forms an important basis for the extraction of curriculum-based measurements, further investigations to assess the diagnostic suitability (e.g., the measurement invariance over different test times) are still pending.

A robust approach to reading recognition of pointer meters based on improved mask-RCNN

In this paper, we address a challenging task in real-word applications, i.e., automatic reading recognition for pointer meters, called PRM.11PRM: Pointer merters Recognition based on Mask-RCNN. This application is valuable in the fields of military, industry, and aerospace. However, the accuracy of recognizing the readings of pointer meters by machine vision is, oftentimes, affected by several factors, such as uneven illumination in each image, large range variation of illumination in different images, complex backgrounds, tilting of pointer meters, image blur, and scale change, resulting in the recognized readings with unacceptable accuracy. In this paper, a new robust approach to reading recognition of pointer meters is proposed. The proposed method consists of three main contributions: (1) constructing a novel deep learning algorithm in which the PrRoIPooling is used in lieu of the RoiAlign in the existing Mask-RCNN, (2) classifying the type of pointer meters while fitting the pointer binary mask, and (3) calculating the readings of pointer meters by the proposed angle method. In addition, we also report and release a new dataset for the community. Experiments show that the new algorithm can significantly improve the accuracy of the recognized readings of pointer meters, meanwhile, the proposed approach is also robust to the natural environments and computationally efficient.

Automatic Audio Based Emotion Recognition System: Scope and Challenges

Emotion is an integral part of human behavior and inherited property in all mode of communication. We, human is well trained thought your experience reading recognition of various emotions which make us more sensible and understandable. But in case of machine, however, it can easily understand content based information such as information in text, audio or video but still far behind to access the depth behind the content. It is need of the era that machine should also be trained to understand emotions correctly for better understanding and to avoid any miscommunication. Present study comes into domain of emotion recognition from audio conversation. Moreover, Audio emotion analysis has many applications in various sectors like healthcare, banking, defense and IT. On the other part, text emotions are easy to decode as there is no role of factors like tone and pitch, but in case of audio emotion analysis both the factors need attention for better accuracy. Also there are several factors like noise, disturbance, and various pauses in communication which results in degrading the accuracy. It is a challenging task to make machine to understand the emotion of the respondents.

Automatic Lip Reading System Based on a Fusion Lightweight Neural Network with Raspberry Pi

Virtual Reality (VR) is a kind of interactive experience technology. Human vision, hearing, expression, voice and even touch can be added to the interaction between humans and machine. Lip reading recognition is a new technology in the field of human-computer interaction, which has a broad development prospect. It is particularly important in a noisy environment and within the hearing- impaired population and is obtained by means of visual information from a video to make up for the deficiency of voice information. This information is a visual language that benefits from Augmented Reality (AR). The purpose is to establish an efficient and convenient way of communication. However, the traditional lip reading recognition system has high requirements of running speed and performance of the equipment because of its long recognition process and large number of parameters, so it is difficult to meet the requirements of practical application. In this paper, the mobile end lip-reading recognition system based on Raspberry Pi is implemented for the first time, and the recognition application has reached the latest level of our research. Our mobile lip-reading recognition system can be divided into three stages: First, we extract key frames from our own independent database, and then use a multi-task cascade convolution network (MTCNN) to correct the face, so as to improve the accuracy of lip extraction. In the second stage, we use MobileNets to extract lip image features and long short-term memory (LSTM) to extract sequence information between key frames. Finally, we compare three lip reading models: (1) The fusion model of Bi-LSTM and AlexNet. (2) A fusion model with attention mechanism. (3) The LSTM and MobileNets hybrid network model proposed by us. The results show that our model has fewer parameters and lower complexity. The accuracy of the model in the test dataset is 86.5%. Therefore, our mobile lip reading system is simpler and smaller than other PC platforms and saves computing resources and memory space.

ANÁLISE DE UM PROGRAMA DE FORMAÇÃO CONTINUADA COM ÊNFASE NA BNCC: AVALIAÇÃO E PARTICIPAÇÃO DOCENTE

Este artigo objetiva analisar a participação, avaliação e entendimentos quanto a implementação da BNCC na área de Ciências da Natureza, levando em consideração um grupo de doze docentes em decorrência da participação no Programa de Formação Continuada: Ensino de Ciências nos Anos Iniciais a partir da BNCC. Para tanto, adota-se a abordagem qualitativa, na forma de estudo de caso, considerando-se os princípios da análise de conteúdo. O referido Programa foi realizado no decorrer do segundo semestre do ano de 2017 e envolveu momentos de diagnóstico inicial, estudos conceituais, análises curriculares e oficinas didáticas, além de avaliação realizada a partir da análise de dados apresentados em um questionário e no diário de bordo da mobilizadora dos encontros. Dentre os resultados, destacam-se a participação ativa e comprometida dos docentes que atuaram de forma colaborativa, o reconhecimento das atividades de leitura e reflexão sobre a BNCC, a aprovação e construção de conhecimento teórico e prático relacionado às unidades temáticas da área das Ciências da Natureza, bem como a troca de experiências e a produção de materiais didáticos.

Performance of complex visual tasks using simulated prosthetic vision via augmented-reality glasses.

Photovoltaic subretinal prosthesis is designed for restoration of central vision in patients with age-related macular degeneration (AMD). We investigated the utility of prosthetic central vision for complex visual tasks using augmented-reality (AR) glasses simulating reduced acuity, contrast, and visual field. AR glasses with blocked central 20° of visual field included an integrated video camera and software which adjusts the image quality according to three user-defined parameters: resolution, corresponding to the equivalent pixel size of an implant; field of view, corresponding to the implant size; and number of grayscale levels. The real-time processed video was streamed on a screen in front of the right eye. Nineteen healthy participants were recruited to complete visual tasks including vision charts, sentence reading, and face recognition. With vision charts, letter acuity exceeded the pixel-sampling limit by 0.2 logMAR. Reading speed decreased with increasing pixel size and with reduced field of view (7°–12°). In the face recognition task (four-way forced choice, 5° angular size) participants identified faces at >75% accuracy, even with 100 μm pixels and only two grayscale levels. With 60 μm pixels and eight grayscale levels, the accuracy exceeded 97%. Subjects with simulated prosthetic vision performed slightly better than the sampling limit on the letter acuity tasks, and were highly accurate at recognizing faces, even with 100 μm/pixel resolution. These results indicate feasibility of reading and face recognition using prosthetic central vision even with 100 μm pixels, and performance improves further with smaller pixels.

Beyond intraocular pressure: Optimizing patient-reported outcomes in glaucoma.

Glaucoma, an irreversible blinding condition affecting 3-4% adults aged above 40 years worldwide, is set to increase with a rapidly aging global population. Raised intraocular pressure (IOP) is a major risk factor for glaucoma where the treatment paradigm is focused on managing IOP using medications, laser, or surgery regimens. However, notwithstanding IOP and other clinical parameters, patient-reported outcomes, including daily functioning, emotional well-being, symptoms, mobility, and social life, remain the foremost concerns for people being treated for glaucoma. These outcomes are measured using objective patient-centered outcome measures (PCOMs) and subjective patient-reported outcome measures (PROMs). Studies using PCOMs have shown that people with glaucoma have several mobility, navigational and coordination challenges; reading and face recognition deficits; and are slower in adapting to multiple real-world situations when compared to healthy controls. Similarly, studies have consistently demonstrated, using PROMs, that glaucoma substantially and negatively impacts on peoples' self-reported visual functioning, mobility, independence, emotional well-being, self-image, and confidence in healthcare, compared to healthy individuals, particularly in those with late-stage disease undergoing a heavy treatment regimen. The patient-centred effectiveness of current glaucoma treatment paradigms is equivocal due to a lack of well-designed randomized controlled trials; short post-treatment follow-up periods; an inappropriate selection or availability of PROMs; and/or an insensitivity of currently available PROMs to monitor changes especially in patients with newly diagnosed early-stage glaucoma. We provide a comprehensive, albeit non-systematic, critique of the psychometric properties, limitations, and recent advances of currently available glaucoma-specific PCOMs and PROMs. Finally, we propose that item banking and computerized adaptive testing methods can address the multiple limitations of paper-pencil PROMs; customize their administration; and have the potential to improve healthcare outcomes for people with glaucoma.