Reading Scene Text in Deep Convolutional Sequences

We develop a Deep-Text Recurrent Network (DTRN) that regards scene text reading as a sequence labelling problem. We leverage recent advances of deep convolutional neural networks to generate an ordered high-level sequence from a whole word image, avoiding the difficult character segmentation problem. Then a deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently. Our model has a number of appealing properties in comparison to existing scene text recognition methods: (i) It can recognise highly ambiguous words by leveraging meaningful context information, allowing it to work reliably without either pre- or post-processing; (ii) the deep CNN feature is robust to various image distortions; (iii) it retains the explicit order information in word image, which is essential to discriminate word strings; (iv) the model does not depend on pre-defined dictionary, and it can process unknown words and arbitrary strings. Codes for the DTRN will be available.Comment: To appear in the 13th AAAI Conference on Artificial Intelligence (AAAI-16), 201

Arabic cursive text recognition from natural scene images

© 2019 by the authors. This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years' publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers

Real-time Online Chinese Character Recognition

In this project, I built a web application for handwritten Chinese characters recognition in real time. This system determines a Chinese character while a user is drawing/writing it. The techniques and steps I use to build the recognition system include data preparation, preprocessing, features extraction, and classification. To increase the accuracy, two different types of neural networks ared used in the system: a multi-layer neural network and a convolutional neural network

The perception and knowledge of cardiovascular risk factors among Chinese Americans

textThe purpose of this study was to evaluate Chinese Americans’ perceptions and knowledge about cardiovascular disease (CVD) risk factors and to determine if acculturation has systematic effects on perception of illness. Perception about the cause, seriousness, curability, and controllability of CVD were investigated. Relationships between the demographic characteristics of the participants and cardiovascular knowledge and perception were examined. The conceptual framework for this study was based on Leventhal’s (1970, 1984) Common Sense Model of Illness Representation. The influence of Kleinman’s Explanatory Model about the cultural and social consideration of illness representation was incorporated. A cross-sectional design was selected for this descriptive study with a convenience sampling technique. The target population was community-based Chinese Americans who live in the United States. Data collection was conducted using the Internet to access a population. The sample of the study was comprised of 124 adults with 68% being female. The majority of participants retained a high Asian identity. Participants identified Chinese over English for speaking, reading, writing preferences. Instruments included the Illness Perception Questionnaire-Revised (IPQ-R), Suinn-Lew Asian Self-Identity Acculturation Scale (SL-ASIA) and the Healthy Heart IQ. Findings included the following: the IPQ-R subscales were intercorrelated in a logical manner. Illness perceptions correlated positively with each other but were negatively correlated with optimistic perceptions like personal and treatment control. No difference was observed in the IPQ-R based on age, gender or educational level. Knowledge of CVD among Chinese Americans was lower than the general population. The level of acculturation had an impact on the illness perception. Acculturation level was significantly related to all seven illness perception dimensions of illness representation on the IPQ-R. There were significant relationships between acculturation level and knowledge of CVD. However, due to the low acculturation level presented by majority of participants, caution must be exercised in the interpretation of the study findings. The findings of this study have important implications for nursing practice, education, and theory. These results also provide directions for future research. Suggestions for health care professionals who care for patients with ethnic cultural backgrounds were given.Nursin

A prior case study of natural language processing on different domain

In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model

Style Transfer and Extraction for the Handwritten Letters Using Deep Learning

How can we learn, transfer and extract handwriting styles using deep neural networks? This paper explores these questions using a deep conditioned autoencoder on the IRON-OFF handwriting data-set. We perform three experiments that systematically explore the quality of our style extraction procedure. First, We compare our model to handwriting benchmarks using multidimensional performance metrics. Second, we explore the quality of style transfer, i.e. how the model performs on new, unseen writers. In both experiments, we improve the metrics of state of the art methods by a large margin. Lastly, we analyze the latent space of our model, and we see that it separates consistently writing styles.Comment: Accepted in ICAART 201