Arif Iqbal Umar

Offline cursive Urdu-Nastaliq script recognition using multidimensional recurrent neural networks

Optical Character Recognition of cursive scripts remains a challenging task due to a large number of character shapes, inter- and intra-word overlaps, context sensitivity and diagonality of text. This paper presents an implicit segmentation based recognition system for Urdu text lines in Nastaliq script. The proposed technique relies on sliding overlapped windows on lines of text and extracting a set of statistical features. The extracted features are fed to a multi-dimensional long short term memory recurrent neural network (MDLSTM RNN) with a connectionist temporal classification (CTC) output layer that labels the character sequences. Experimental study of the proposed technique is carried out on the standard Urdu Printed Text-line Images (UPTI) database which comprises 10,000 text lines in Nastaliq font. Evaluations under different experimental settings realize promising recognition rates with a highest character recognition rate of 96.40%.

Segmentation techniques for recognition of Arabic-like scripts: A comprehensive survey

Arabic script based text recognition system has been a popular field of research for many years that can be used in the learning and teaching process to the students and educators how to read and understand educational contents of Arabic script. The challenging nature of Arabic script recognition has attracted the attention of researchers from both industry and academic circles but these efforts have not achieved good results until now. Segmentation of Urdu script when written in Nasta’liq writing style is very difficult task due to the complexity of writing style as compare to Naskh writing style. Good segmentation is one of the reasons for high accuracy. Character segmentation has been a critical phase of the OCR process. The higher recognition rates for isolated characters as compare to results of words or connected character well illustrate the importance of segmentation. Current study investigates the recent work for character segmentation and challenges for segmentation for Arabic script based languages.

Efficient leukocyte segmentation and recognition in peripheral blood image

BACKGROUND Blood cell count, also known as differential count of various types of blood cells, provides valuable information in order to assess variety of diseases like AIDS, leukemia and blood cancer. Manual techniques are still used in diseases diagnosis that is very lingering and tedious process. However, machine based automatic analysis of leukocyte is a powerful tool that could reduce the human errors, improve the accuracy, and minimize the required time for blood cell analysis. However, leukocyte segmentation is a challenging process due to the complexity of the blood cell image; therefore, this task remains unresolved issue in the blood cell segmentation. OBJECTIVE The aim of this work is to develop an efficient leukocyte cell segmentation and classification system. METHODS This paper presents an efficient strategy to segment cell images. This has been achieved by using Wiener filter along with Curvelet transform for image enhancement and noise elimination in order to elude false edges. We have also used combination of entropy filter, thresholding and mathematical morphology for obtaining image segmentation and boundary detection, whereas we have used back-propagation neural network for leukocyte classification into its sub classes. RESULTS As a result, the generated segmentation results are fruitful in a sense that we have overcome the problem of overlapping cells. We have obtained 100%, 96.15%, 92.30%, 92.30% and 96.15% accuracy for basophil, eosinophil, monocyte, lymphocyte and neutrophil respectively.

An efficient and secure mobile phone voting system

Electronic voting system provides convenience and access to the electorate without the geographical restrictions. Mobile phone is one of the emerging technologies to perform e-voting with democratic norms and privacy concern. In this paper we suggest a mobile phone voting protocol based on hybrid cryptosystem. Protocol consists of three phases: online registration; vote casting and vote collecting and result phase. Proposed protocol provides secure and efficient online vote casting and can also be implemented parallel with paper ballot voting system. Proposed protocol has efficiency, security and deployable in developing countries due to its reliance on SMS messaging without requiring internet connectivity.

Urdu Nastaliq recognition using convolutionalrecursive deep learning

Recent developments in recognition of cursive scripts rely on implicit feature extraction methods that provide better results as compared to traditional hand-crafted feature extraction approaches. We present a hybrid approach based on explicit feature extraction by combining convolutional and recursive neural networks for feature learning and classification of cursive Urdu Nastaliq script. The first layer extracts low-level translational invariant features using Convolutional Neural Networks (CNN) which are then forwarded to Multi-dimensional Long Short-Term Memory Neural Networks (MDLSTM) for contextual feature extraction and learning. Experiments are carried out on the publicly available Urdu Printed Text-line Image (UPTI) dataset using the proposed hierarchical combination of CNN and MDLSTM. A recognition rate of up to 98.12% for 44-classes is achieved outperforming the state-of-the-art results on the UPTI dataset.

Urdu Nasta’liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networks

The recognition of Arabic script and its derivatives such as Urdu, Persian, Pashto etc. is a difficult task due to complexity of this script. Particularly, Urdu text recognition is more difficult due to its Nasta’liq writing style. Nasta’liq writing style inherits complex calligraphic nature, which presents major issues to recognition of Urdu text owing to diagonality in writing, high cursiveness, context sensitivity and overlapping of characters. Therefore, the work done for recognition of Arabic script cannot be directly applied to Urdu recognition. We present Multi-dimensional Long Short Term Memory (MDLSTM) Recurrent Neural Networks with an output layer designed for sequence labeling for recognition of printed Urdu text-lines written in the Nasta’liq writing style. Experiments show that MDLSTM attained a recognition accuracy of 98% for the unconstrained Urdu Nasta’liq printed text, which significantly outperforms the state-of-the-art techniques.

Authenticated key agreement and cluster head selection for Wireless Body Area Networks

Wireless Body Area Network (WBAN) has become imperative due to rapid advancement in medical technology. However, WBAN faces different security issues due to open air communication of information. In this paper, we have proposed a lightweight smart crypto solution using authenticated key exchange coupled with cluster head formation and selection for the security of WBAN. Our proposed solution logically combines cluster head selection with key agreement that fulfills the security requirement of wireless body area network, efficient in term of resource utilization.

Accurate Microscopic Red Blood Cell Image Enhancement and Segmentation

Erythrocytes (RBC) are the most common type of blood cell. These cells are responsible for the delivery of oxygen to body tissues. The abnormality in erythrocyte cell affects the physical properties of red cell. It may also decrease the life span of red blood cells which may lead to stroke, anemia and other fatal diseases. Until now, Manual techniques are in practiced for diagnosis of blood cell’s diseases. However, this traditional method is tedious, time consuming and subject to sampling error. The accuracy of manual method depends on the expertise of the expert, while the accuracy of automated analyzer depends on the segmentation of objects in microscopic image of blood cell. Despite numerous efforts made for accurate blood cells image segmentation and cell counting in the literature. Still accurate segmentation is difficult due to the complexity of overlapping objects and shapes in microscopic images of blood cells. In this paper we have proposed a novel method for the segmentation of blood cells. We have used wiener filter along with Curvelet transform for image enhancement and noise removal. The snake algorithm and Gram-Schmidt orthogonalization have applied for boundary detection and image segmentation, respectively.

Content-Based Image Retrieval Using Texture Color Shape and Region

Interests to accurately retrieve required images from databases of digital images are growing day by day. Images are represented by certain features to facilitate accurate retrieval of the required images. These features include Texture, Color, Shape and Region. It is a hot research area and researchers have developed many techniques to use these feature for accurate retrieval of required images from the databases. In this paper we present a literature survey of the Content Based Image Retrieval (CBIR) techniques based on Texture, Color, Shape and Region. We also review some of the state of the art tools developed for CBIR.

Extreme learning machine based microscopic red blood cells classification

The digitalization of blood slides introduced pathology to a new era. Despite being the most powerful prognostic tool; automated analysis of microscopic blood smear images is still not used in routine clinical practices as manual pathological image analysis methods are still in use that is tedious, time consuming and subjective to technician dependent variation, furthermore it also needs training and skills. In this work, we present novel method based on extreme machine learning approach for the classification of red blood cells (RBC) images. Segmentation of RBC is initiated with statistical based thresholding to retrieve those pixels which are most relevant to RBC followed by Fuzzy C-means for the image segmentation and boundary detection. Different texture and geometrical features are extracted for the classification of normal and abnormal cells. The classification technique is rigorously evaluated against the dataset to evaluate the accuracy of classifier. We have compared the results with state of the art techniques. So far the proposed technique has produced more promising results as compared to the existing techniques.