Several hundred thousands of tons of ZnO are used by per year, e.g. as an additive to concrete or to rubber. In the field of optoelectronics, ZnO holds promises as a material for a blue/UV optoelectronics, alternatively to GaN, as a cheap, transparent, conducting oxide, as a material for electronic circuits, which are transparent in the visible or for semiconductor spintronics. The main problem is presently, however, a high, reproducible and stable p-doping. We review in this contribution partly critically the material growth, fundamental properties of ZnO and of ZnO-based nanostructures, doping as well as present and future applications, with emphasis on the electronic and optical properties including stimulated emission. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

ZnO : From basics towards applications

ZnO is presently experiencing a research boom with more than 2000 ZnO-related publications in 2005. This phenomenon is triggered, for example, by hope to use ZnO as a material for blue/UV optoelectronics as an alternative to GaN, as a cheap, transparent, conducting oxide, as a material for electronic circuits that are transparent in the visible or for semiconductor spintronics. Currently, however, the main problem is to achieve high, reproducible and stable p-doping. Herein, we critically review aspects of the material growth, fundamental properties of ZnO and ZnO-based nanostructures and doping as well as present and future applications with emphasis on the electronic and optical properties including stimulated emission.

ZnO: Material, Physics and Applications

he Arabic language has recently become the focus of an increasing number of projects in natural language processing (NLP) and computational linguistics (CL). In this book, I try to provide NLP/CL system developers and researchers (computer scientists and linguists alike) with the necessary background information for working with Arabic.I discuss various Arabic linguistic phenomena and review the state-of-the-art in Arabic processing.

Introduction to Arabic Natural Language Processing

The random laser represents a non-conventional laser whose feedback is mediated by random fluctuation of the dielectric constant in space. Depending on whether the feedback is intensity or field feedback, random lasers are classified into two categories: a random laser with incoherent and non-resonant feedback, and a random laser with coherent and resonant feedback. This paper reviews some of the latest developments in the latter, including experiments and theories on the 'classical' and 'quantum' type of random lasers with coherent feedback, the photon localization lasers. The quantum theories of random lasers are briefly introduced, followed by a discussion of the recent studies on the interplay between light localization and coherent amplification.

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Review on latest developments in random lasers with coherent feedback

In this paper we explore a new model focused on integrating two classifiers; Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for offline Arabic handwriting recognition (OAHR) on which the dropout technique was applied. The suggested system altered the trainable classifier of the CNN by the SVM classifier. A convolutional network is beneficial for extracting features information and SVM functions as a recognizer. It was found that this model both automatically extracts features from the raw images and performs classification. Additionally, we protected our model against over-fitting due to the powerful performance of dropout. In this work, the recognition on the handwritten Arabic characters was evaluated; the training and test sets were taken from the HACDB and IFN/ENIT databases. Simulation results proved that the new design based-SVM of the CNN classifier architecture with dropout performs significantly more efficiently than CNN based-SVM model without dropout and the standard CNN classifier. The performance of our model is compared with character recognition accuracies gained from state-of-the-art Arabic Optical Character Recognition, producing favorable results.

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A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition

Many feature extraction approaches for off-line handwriting recognition (OHR) rely on accurate binarization of gray-level images. However, high-quality binarization of most real-world documents is extremely difficult due to varying characteristics of noises artifacts common in such documents. Unlike most of these features, Gabor features do not require binarization of the document images, and thus are likely to be more robust to noises in document images. To demonstrate the efficacy of our proposed Gabor features, we perform subword recognition for off-line Arabic handwritten images using Support Vector Machines (SVM). We also compare the recognition performance with other binarization based features which have been proven to be effective in capturing shape characteristics of handwritten Arabic subwords, such as GSC (a set of gradient, structure, and concavity features) and skeleton based Graph features. Our preliminary experimental results show that Gabor features outperform Graph features and are slightly better than GSC features for Arabic subword recognition. In addition, by combining Gabor and GSC features, we obtain a significant reduction in classification error rate over using GSC or Gabor features alone.

Gabor features for offline Arabic handwriting recognition

Camera-captured optical character recognition (OCR) is a challenging area because of artifacts introduced during image acquisition with consumer-domain hand-held and Smart phone cameras. Critical information is lost if the user does not get immediate feedback on whether the acquired image meets the quality requirements for OCR. To avoid such information loss, we propose a novel automated image quality assessment method that predicts the degree of degradation on OCR. Unlike other image quality assessment algorithms which only deal with blurring, the proposed method quantifies image quality degradation across several artifacts and accurately predicts the impact on OCR error rate. We present evaluation results on a set of machine-printed document images which have been captured using digital cameras with different degradations.

Automated image quality assessment for camera-captured OCR

Offline handwriting recognition of free-flowing Arabic text is a challenging task due to the plethora of factors that contribute to the variability in the data. In this paper, we address some of these sources of variability, and present experimental results on a large corpus of handwritten documents. Specific techniques such as the application of context-dependent Hidden Markov Models (HMMs) for the cursive Arabic script, unsupervised adaptation to account for the stylistic variations across scribes, and image pre-processing to remove ruled-lines are explored. In particular, we proposed a novel integration of structural features in the HMM framework which exclusively results in a 9% relative improvement in performance. Overall, we demonstrate a relative reduction of 17% in word error rate over our baseline Arabic handwriting recognition system.

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Improvements in BBN's HMM-Based Offline Arabic Handwriting Recognition System

Document image binarization is one of the critical initial steps for document analysis and understanding Previous work mostly focused on exploiting hand-crafted features to build statistical models for distinguishing text from background However, these approaches only achieved limited success because: (a) the effectiveness of hand-crafted features is limited by the researcher's domain knowledge and understanding on the documents, and (b) a universal model cannot always capture the complexity of different document degradations In order to address these challenges, we propose a convolutional encoder-decoder model with deep learning for document image binarization in this paper In the proposed method, mid-level document image representations are learnt by a stack of convolutional layers, which compose the encoder in this architecture Then the binarization image is obtained by mapping low resolution representations to the original size through the decoder, which is composed by a series of transposed convolutional layers We compare the proposed binarization method with other binarization algorithms both qualitatively and quantitatively on the public dataset The experimental results show that the proposed method has comparable performance to the other hand-crafted binarization approaches and has more generalization capabilities with limited in-domain training data

Using Convolutional Encoder-Decoder for Document Image Binarization

We present a novel end-to-end trainable OCR system combining a CNN for feature extraction with 1-D LSTMs for sequence modeling. We present results on English and Arabic handwriting data, and on English machine print data, showing state-of-the-art performance. We believe that our method is simpler than existing 2D LSTM models, and will make it easier to use techniques borrowed from CNN research in computer vision to improve OCR performance.

Huaigu Cao

Papers

Gabor features for offline Arabic handwriting recognition

Automated image quality assessment for camera-captured OCR

Improvements in BBN's HMM-Based Offline Arabic Handwriting Recognition System

Using Convolutional Encoder-Decoder for Document Image Binarization

Combining Convolutional Neural Networks and LSTMs for Segmentation-Free OCR