The current research on English reading is mainly based on the sense of reading questions, reading patterns, answering skills, etc.
#Arabic letters how to#
Therefore, how to improve English reading ability has also become a focus area of school education and students. It has become the consensus of society as a subject of education in primary and secondary schools and even universities. The results show that the Gaussian mixture feature sparse representation optimization model based on convolution neural network has the advantages of high feasibility, high data accuracy and high response speed, which can enhance the processing efficiency of vehicle detection image and improve the utilization of local environmental information in the image.Įnglish is a universal language in the world. The model can extract the feature information of the vehicle detection image better by making the scheme of the real-time vehicle detection image and according to the image features and convolution neural network algorithm. The vehicle image data are collected from many aspects, and the convolution neural network is used for comprehensive analysis and evaluation. Based on this, this paper studies a new pattern of sparse representation optimization of image Gaussian mixture feature based on convolution neural network, and designs a sparse representation system model of vehicle detection image based on convolution neural network. With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Experimental analyses on HAMCDB and AHCD databases, versus existing methods demonstrate the efficacy of the presented framework, and our framework’s performance is promising, achieving accuracies of 98.63% and 98.95% using Gabor filters for HAMCDB and AHCD respectively, and 100% with HOG and BSIF. These features are finally fed to a classification process based on nearest neighbor’s classifier and cosine Mahalanobis distance to obtain final character labels. We propose a new and efficient Arabic handwritten character recognition scheme, tested on two datasets the new proposed dataset HAMCDB compared to the public database AHCD of handwritten Arabic characters, where the local features using Gabor filter, Histogram of Oriented Gradients (HOG) and Binarized Statistical Image Features (BSIF) are enhanced with a deep auto-encoder architecture for better accuracy. HAMCDB understands a total of 1560 character images with 78 shapes and 20 images for each one.
#Arabic letters download#
The samples in the database were obtained from a variety of sources, the most important of which was the Algerian manuscripts portal (), which is a platform designed by the work team of the Algerian Manuscript’s laboratory in Africa, to safeguard the humanitarian patrimony, where the reader can view and download digital copies and through this work by offering an OCR that is specific to the style of our region, we hope to make the search’s operation more easier. The database consists of all shapes of Arabic characters written in the Maghrebi style. One of the main contributions of this paper is to create and present in detail a new database for Handwritten Arabic-Maghrebi Characters (HAMCDB), this handwriting style, which is represented and used for the first time in this field of character recognition and is much more difficult, poses additional challenges and complexities due to its characteristics. However, optical Arabic character recognition systems still suffer from low performances in the wild because of high human handwriting variations, styles, ambiguity and complexity. Over the years, automated handwritten Arabic character recognition systems have evolved.
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