In recent years, lane detection has become one of the most important factors in the progress of intelligent vehicles. To deal with the challenging problem of low detection precision and real-time performance of most traditional systems, we proposed a real-time deep lane detection system based on CNN Encoder–Decoder and Long Short-Term Memory (LSTM) networks for dynamic environments and complex road conditions. The CNN Encoder network is used to extract deep features from a dataset and to reduce their dimensionality. A corresponding decoder network is used to map the low resolution encoder feature maps to dense feature maps that correspond to road lane. The LSTM network processes historical data to improve the detection rate through the removal of the influence of false alarm patches on detection results. We propose three network architectures to predict the road lane: CNN Encoder–Decoder network, CNN Encoder–Decoder network with the application of Dropout layers and CNN Encoder–LSTM-Decoder network that are trained and tested on a public dataset comprising 12764 road images under different conditions. Experimental results show that the proposed hybrid CNN Encoder–LSTM-Decoder network that we have integrated into a Lane-Departure-Warning-System (LDWS) achieves high prediction performance namely an average accuracy of 96.36%, a Recall of 97.54%, and a F1-score of 97.42%. A NVIDIA Jetson Xavier NX supercomputer has been used, for its performance and efficiency, to realize an Embedded Deep LDWS. 

Deep embedded hybrid CNN-LSTM network for lane detection on NVIDIA Jetson Xavier NX

A Quranic optical character recognition (OCR) system based on convolutional neural network (CNN) followed by recurrent neural network (RNN) is introduced in this work. Six deep learning models are built to study the effect of different representations of the input and output, and the accuracy and performance of the models, and compare long short-term memory (LSTM) and gated recurrent unit (GRU). A new Quranic OCR dataset is developed based on the most famous printed version of the Holy Quran (Mushaf Al-Madinah), and a page and line-text image with the corresponding labels is prepared. This work’s contribution is a Quranic OCR model capable of recognizing the Quranic image’s diacritic text. A better performance in word recognition rate (WRR) and character recognition rate (CRR) is achieved in the experiments. The LSTM and GRU are compared in the Arabic text recognition domain. In addition, a public database is built for research purposes in Arabic text recognition that contains the diacritics and the Uthmanic script, and is large enough to be used with the deep learning models. The outcome of this work shows that the proposed system obtains an accuracy of 98% on the validation data, and a WRR of 95% and a CRR of 99% in the test dataset.

/pdf/quranic-optical-text-recognition-using-deep-learning-models-1m05ya8wl0.pdf

Quranic Optical Text Recognition Using Deep Learning Models

Object classification, such as handwritten Arabic character recognition, is a computer vision application. Deep learning techniques such as convolutional neural networks (CNNs) are employed in character recognition to overcome the processing complexity with traditional methods. Usually, a CNN is followed by an activation function such as a rectified linear unit (ReLU) or leaky ReLU to filter the extracted features. Most handwritten character recognition endures an imbalanced number of positive and negative vectors. This issue decreases CNN performance when adopting ReLU and leaky ReLU for the next deep layers in the architecture. Hence, this study proposed an optimized leaky ReLU to retain more negative vectors using a CNN architecture with a batch normalization layer to address this weakness. To evaluate the proposed method, four datasets are used: Arabic Handwritten Characters Dataset (AHCD), self-collected, Modified National Institute of Standards and Technology (MNIST), and AlexU Isolated Alphabet (AIA9K). The proposed method shows significant performance in terms of accuracy, precision, and recall measures compared to the state-of-art methods. The results showed outstanding improvement over the known leaky ReLU as follows: 99% for AHCD, 95.4% for self-collected data, 90% for HIJJA dataset and 99% for Digit MNIST. The proposed CNN architecture with the proposed optimized leaky ReLU showed a stable accuracy performance and error rates between the training, validation, and testing phases. This indicates that most samples are trained and classified correctly.

Optimized leaky ReLU for handwritten Arabic character recognition using convolution neural networks

An efficient energy management system is integrated with the power grid to collect information about the energy consumption and provide the appropriate control to optimize the supply–demand pattern. Therefore, there is a need for intelligent decisions for the generation and distribution of energy, which is only possible by making the correct future predictions. In the energy market, future knowledge of the energy consumption pattern helps the end-user to decide when to buy or sell the energy to reduce the energy cost and decrease the peak consumption. The Internet of things (IoT) and energy data analytic techniques have provided the convenience to collect the data from the end devices on a large scale and to manipulate all the recorded data. Forecasting an electric load is fairly challenging due to the high uncertainty and dynamic nature involved due to spatiotemporal pattern consumption. Existing conventional forecasting models lack the ability to deal with the spatio-temporally varying data. To overcome the above-mentioned challenges, this work proposes an encoder–decoder model based on convolutional long short-term memory networks (ConvLSTM) for energy load forecasting. The proposed architecture uses encode consisting of multiple ConvLSTM layers to extract the salient features in the data and to learn the sequential dependency and then passes the output to the decoder, having LSTM layers to make forecasting. The forecasting results produced by the proposed approach are favorably comparable to the existing state-of-the-art and better than the conventional methods with the least error rate. Quantitative analyses show that a mean absolute percentage error (MAPE) of 6.966% for household energy consumption and 16.81% for city-wide energy consumption is obtained for the proposed forecasting model in comparison with existing encoder–decoder-based deep learning models for two real-world datasets.

Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System

The Ottoman OCR is an open problem because the OCR models for Arabic do not perform well on Ottoman. The models specifically trained with Ottoman documents have not produced satisfactory results either. We present a deep learning model and an OCR tool using that model for the OCR of printed Ottoman documents in the naksh font. We propose an end‐to‐end trainable CRNN architecture consisting of CNN, RNN (LSTM), and CTC layers for the Ottoman OCR problem. An experimental comparison of this model, called Osmanlica.com, with the Tesseract Arabic, the Tesseract Persian, Abby Finereader, Miletos, and Google Docs OCR tools or models was performed using a test data set of 21 pages of original documents. With 88.86% raw text, 96.12% normalized text, and 97.37% joined text character recognition accuracy, the Osmanlica.com Hybrid model outperforms the others with a marked difference. Our model outperforms the next best model by a clear margin of 4% which is a significant improvement considering the difficulty of the Ottoman OCR problem, and the huge size of the Ottoman archives to be processed. The hybrid model also achieves 58% word recognition accuracy on normalized text which is the only rate above 50%.

A deep learning model for Ottoman OCR

Building a robust Optical Character Recognition (OCR) system for languages, such as Arabic with cursive scripts,
has always been challenging These challenges increase if the text contains diacritics of different sizes for
characters and words Apart from the complexity of the used font, these challenges must be addressed in
recognizing the text of the Holy Quran To solve these challenges, the OCR system would have to undergo
different phases Each problem would have to be addressed using different approaches, thus, researchers are
studying these challenges and proposing various solutions This has motivate this study to review Arabic OCR
dataset because the dataset plays a major role in determining the nature of the OCR systems State-of-the-art
approaches in segmentation and recognition are discovered with the implementation of Recurrent Neural
Networks (Long Short-Term Memory-LSTM and Gated Recurrent Unit-GRU) with the use of the Connectionist
Temporal Classification (CTC) This also includes deep learning model and implementation of GRU in the Arabic
domain This paper has contribute in profiling the Arabic text recognition dataset thus determining the nature of
OCR system developed and has identified research direction in building Arabic text recognition dataset

/pdf/a-review-of-arabic-text-recognition-dataset-4zsphzhpqk.pdf

A review of Arabic text recognition dataset

Any text recognition or Optical Character Recognition (OCR) system requires a dataset to learn how to recognize the text. Due to the lack of a standard benchmark, most of the studies in this field were conducted using private datasets without a fair comparison. In this work, we used the standard Mushaf al Madinah benchmark where there are some rules in writing style, for example, the page should start with the beginning of verse and end with the end of verse. Following these rules make the words vary in size and paragraphs on different pages. These characteristics making the recognition of the Quranic text more challenging than the normal Arabic text, where the state of the art systems fails to recognize the Quranic text. Therefore, Quranic OCR dataset is presented in this study. It contains 604 images on page level and 8927 images in text-line level. This dataset is public and free to use for the research community. The Quranic dataset would help the researchers in the field of Arabic OCR where the dataset produced in this study would be made public and free for the use of research purposes.

/pdf/a-quranic-dataset-for-text-recognition-2rv7amihd4.pdf

Idris Al-Sheikh

Papers

Quranic Optical Text Recognition Using Deep Learning Models

A review of Arabic text recognition dataset

A Quranic Dataset for Text Recognition