Sana Saeed

Bio: Sana Saeed is an academic researcher. The author has contributed to research in topics: Latin script & Cursive. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

An application of deep learning in character recognition: an overview

Abstract: For automated document analysis, OCR (Optical character recognition) is a basic building block. The robust automated document analysis system can have impact over a wider sphere of life. Many of the researchers have been working hard to build OCR systems in various languages with significant degree of accuracy, character recognition rate and minimum error rate. Deep learning is the start of art technique with efficient and accurate result as compared to other techniques. Every language, moreover every script have its own challenges e.g. scripts where characters are well separated are less challenging as compared to cursive scripts where characters are attached with one another. In this chapter, we would take a detailed account of the state of art deep learning techniques for Arabic like script, Latin script and symbolic script.

Deep optical character recognition: a case of Pashto language

Abstract: Over the past decades, text recognition technologies have focused immensely on noncursive isolated scripts. A text recognition system for the cursive Pashto script will serve as a great contribution, allowing the traditional, cultural, and educational Pashto literature to be converted into machine-readable form. We propose the use of deep learning architectures based on the transfer learning for the recognition of Pashto ligatures. For recognition analysis and evaluation, the ligature images in the dataset are preprocessed by data augmentation techniques, i.e., negatives, contours, and rotated to increase the variation of each sample and size of the original dataset. Rich feature representations are automatically extracted from the Pashto ligature images using deep convolution layers of the convolution neural network (CNN) architectures using fine-tuned approach. Pretrained CNN architectures: AlexNet, GoogleNet, and VGG (VGG-16 and VGG-19) are used for classification by feeding the extracted features to a fully connected layer and a softmax layer. The proposed deep transfer-based learning has achieved phenomenal recognition rates for Pashto ligatures on benchmark FAST-NU Pashto dataset. An accuracy of 97.24%, 97.46%, and 99.03% is achieved using AlexNext, GoogleNet, and VGGNet architectures, respectively.

A Hybrid Deep Neural Network for Urdu Text Recognition in Natural Images

Abstract: In this work, we present a benchmark and a hybrid deep neural network for Urdu Text Recognition in natural scene images. Recognizing text in natural scene images is a challenging task, which has attracted the attention of computer vision and pattern recognition communities. In recent years, scene text recognition has widely been studied where; state-of-the-art results are achieved by using deep neural network models. However, most of the research works are performed for English text and a less concentration is given to other languages. In this paper, we investigate the problem of Urdu text recognition in natural scene images. Urdu is a type of cursive text written from right to left direction where, two or more characters are joined to form a word. Recognizing cursive text in natural images is considered an open problem due to variations in its representation. A hybrid deep neural network architecture with skip connections, which combines convolutional and recurrent neural network, is proposed to recognize the Urdu scene text. We introduce a new dataset of 11500 manually cropped Urdu word images from natural scenes and show the baseline results. The network is trained on the whole word image avoiding the traditional character based classification. Data augmentation technique with contrast stretching and histogram equalizer is used to further enhance the size of the dataset. The experimental results on original and augmented word images show state-of-the-art performance of the network.

Machine Learning and Marketing: A Systematic Literature Review

Abstract: Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008–2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis—content analysis.

Neural Computing for Online Arabic Handwriting Character Recognition using Hard Stroke Features Mining.

Abstract: Online Arabic cursive character recognition is still a big challenge due to the existing complexities including Arabic cursive script styles, writing speed, writer mood and so forth. Due to these unavoidable constraints, the accuracy of online Arabic character's recognition is still low and retain space for improvement. In this research, an enhanced method of detecting the desired critical points from vertical and horizontal direction-length of handwriting stroke features of online Arabic script recognition is proposed. Each extracted stroke feature divides every isolated character into some meaningful pattern known as tokens. A minimum feature set is extracted from these tokens for classification of characters using a multilayer perceptron with a back-propagation learning algorithm and modified sigmoid function-based activation function. In this work, two milestones are achieved; firstly, attain a fixed number of tokens, secondly, minimize the number of the most repetitive tokens. For experiments, handwritten Arabic characters are selected from the OHASD benchmark dataset to test and evaluate the proposed method. The proposed method achieves an average accuracy of 98.6% comparable in state of art character recognition techniques.