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This study aims to identify the significance of driver’s socioeconomic demographics (SEDs) in the decision to speed and crash involvement. A questionnaire was designed consisting of a driver’s SEDs, speeding propensity, and crash experience. This questionnaire was conducted with the students and employees of the University of Nizwa and other drivers at the selected locations. A total of 604 usable samples were obtained. Simple frequency distribution and discriminant multivariate analysis were conducted on the driver’s responses. Survey results revealed that about 47.7% of the drivers have experienced a crash. The driver’s gender nationality, profession, age, type of vehicle drive, driving experience, and past crash experience are significant attributes of the driver’s speeding behavior. Ordered probit analysis for speeding behavior and simple probit regression analysis for crash involvement was conducted. The male drivers and those who are under the age of 30 years and have driving experience of more than 3 years have more likelihood to exceed the speed limits than other drivers. Similarly, the driver’s gender, age (≤ 30 years), and those who are employees have a significant correlation with the propensity of crash involvement. Male and young drivers have more likelihood to be involved in a crash.
Optical Character Recognition (OCR) is one of the most investigated pattern classification problems that has received remarkable research attention for more than half a century. From the simplest systems recognizing isolated digits to end-to-end recognition systems, applications of OCRs vary from postal mail sorting to reading systems in scene images facilitating autonomous navigation or assisting the visually impaired. Despite tremendous research endeavors and availability of commercial recognition engines for many scripts, recognition of cursive scripts still remains an open and challenging research problem mainly due to the complexity of script, segmentation issues and large number of classes to recognize. Among these, Urdu makes the subject of our study. More specifically, this study investigates the recognition of printed Urdu text in Nastaliq style, the most widely employed script for Urdu text that is more complex than the Naskh style of Arabic. This work presents a holistic (segmentation-free) technique that exploits ligatures (partial words) as units of recognition. Urdu has a total of more than 26,000 unique ligatures, many of the ligatures, however, share the same main body (primary ligature) and differ only in the number and position of dots and diacritics (secondary ligatures). We exploit this idea to separately recognize the primary and secondary ligatures and later re-associate the two to recognize the complete ligature. Recognition is carried out using two techniques; the first of these is based on hand-crafted statistical features using hidden Markov models (HMMs). Features extracted using sliding windows are used to train a separate model for each ligature class. Feature sequences of the query ligature are fed to all the models and recognition is carried out through the model that reports the maximum probability. The second technique employs Convolutional Neural Networks (CNNs) to automatically extract useful feature representations from the classes and recognize the ligatures. We investigated the performance of a number of pre-trained networks using transfer learning techniques and trained our own set of networks from scratch as well. Experimental study of the system is carried out on two benchmark datasets of Urdu text, the ‘Urdu Printed Text Images’ (UPTI) database and the ‘Center of Language Engineering’ (CLE) database. A number of experimental scenarios are considered for system evaluation and the realized recognition rates are compared with state-of-the-art recognition systems for printed Urdu text. An interesting aspect of experimental study is the combination of unique ligatures in the two datasets to generate a large set of around 2800 unique primary and secondary ligatures covering a major proportion of the Urdu corpus. The system reports high classification rates (88.10% and 94.78% on CLE and UPTI query ligatures respectively) demonstrating the effectiveness of the proposed recognition techniques which can be adapted for other cursive scripts as well. The findings of this study are expected to be useful for the document recognition community in general and researchers targeting cursive scripts in particular.