NUST Institutions Library Catalogue Search for 'an:"121640"'

By Ali, Ruqaiya . . 62p. , Speech and hearing impairment is a condition that limits a person's capacity to communicate verbally and audibly. Individuals who are impacted by this adopt sign language and various alternative forms of communication. Even though sign language has become more widely used recently, it is still difficult for non-signers to engage with the individuals that use sign language. There has been promising improvement in the disciplines of motion and gesture detection combining techniques of computer vision and deep learning. This study aims to put forward an approach that uses deep learning techniques to automate the recognition of American Sign Language, thereby lowering barriers to effective communication among the hard of hearing individuals and hearing communities. Previously, several techniques of deep learning were employed for sign language gesture recognition. Video sequences are used as an input for extraction of spatial and temporal information. Word-level sign language recognition (WSLR) technology advancements can drastically reduce the necessity for human translators and enable the signers and non-signers to easily communicate. The majority of methods currently in use rely on the use of extra equipment like sensor devices, gloves, or depth cameras. The ease of usage in real life situations is, however, constrained by these limitations. Such situations may benefit from deep learning techniques that are entirely vision-based and non-intrusive. American Sign Language has its own rules for syntax and grammar, much like any other spoken language. ASL, like every other language, is a living language that evolves and develops through time. The majority of ASL users are found in both Canada and the United States of America. In order to complete their current and "international" degree requirements, most schools and institutions across the US accept ASL. This study uses deep learning methods to predict American Sign Language using the WLASL (word-level American Sign Language) dataset. For the dataset, a subset of 50 classes was chosen from WLASL. This study used a combination of VGG16-LSTM and ConvLSTM based to work with spatio-temporal features. These models were chosen due to their ability to work with spatial and temporal features. We observed that VGG16-LSTM outperformed the ConvLSTM architecture. Both models' performances are examined using accuracy as an evaluation metric and judged according to how well they perform on test videos. 30cm.

Place Hold on DEEP LEARNING BASED SIGN LANGUAGE PREDICTION /

Stream Classification & Brand Recognition for Media Analysis /

By Ijaz, M Adeel . . 55p. 30cm.

Place Hold on Stream Classification & Brand Recognition for Media Analysis /

A1 Based Automation Gating of B-Cells in Flow Cytometry /

By Nadeem, Talha. . 50p. 30cm.

Place Hold on A1 Based Automation Gating of B-Cells in Flow Cytometry /

Urdu Text Recognition in TV Channel Streams /

By Afzal, Hira . . 88p. , Text recognition from images has received a lot of attention of the researchers for over three decades. It is one of the most worked upon problem in the domain of pattern classification. The optical character recognitions systems can be used to recognize numeric digits, alphabets, words and sentences in any language and can be applied to recognize scenic text for the assistance of visually impaired persons. These systems can also be used for the assisted navigation of autonomous vehicles. Despite all the related work that has been done for the Latin scripts, the recognition of text written in non-Latin languages like Urdu, Arabic, Pashto etc. has always been a challenging task due to complex cursive nature of the script. Urdu OCR systems can be used for digitization of the data, which will further allow us to use it for search and retrieval of the specific information. These kinds of systems grant us the easy access to content based information retrieval. Extracting embedded text from the images is an active area of research in the community of Document analysis as well. The currently available OCR frameworks mainly focus on the recognition of Latin texts like English script etc. and they cannot be applied for non-Latin languages. So we are looking to implement a solution based on deep framework for the line level recognition of Urdu text, to extract useful information from the news tickers. In this work, we are focusing to design an end-to-end system that will detect and recognize the Urdu text embedded in TV channel streams that is commonly written in Nasta‘liq scripting style. The development of Urdu OCR systems consist mainly of two subtasks, text detection and text recognition. For the development of robust recognition systems, the availability and access of a huge quantity of annotated data is the first and the foremost requirement. So, the dataset used here has been collected from different news channels and is comprehensive enough to cover the low and high resolution images. It includes the distorted, low quality as well as faded news tickers making it ideal for testing the performance of any Urdu News OCR system. Once the text has been detected or localized in an image, it can be cropped and used for the recognition part. For the recognition task, a language independent Convolutional Recurrent Neural Network (CRNN) based end-to-end architecture has been proposed with CTC loss function for the line level recognition of Urdu text embedded in the news tickers of TV channel streams. In this proposed system, a large number of different techniques have been used for data augmentation. These kinds of data variations will prevent the model from over fitting and help it to generalize better. Finally, the results of this approach have been presented on the test set. The achieved results are 0.63% CER, 6.43% WER, 5.14% LER and levenshtein distance of 0.02 on the Urdu Ticker Text dataset. These results indicate that our proposed methodology has shown outstanding performance as compared to the commercially available recognition systems and this proposed methodology can be applied to a variety of other non-Latin scripts as well. In this thesis, we also discussed the common problems faced when dealing with the low-resource language recognizers like Urdu and Arabic etc. The outcomes of this study are expected to be applicable and useful for the researchers, working on the recognition of non-Latin languages written in cursive scripting style. 30cm.

Place Hold on Urdu Text Recognition in TV Channel Streams /

Audio Visual Person Recognition /

By Ali, Ahmad . . 46p. , Person authentication is a primary element to consider wherever privacy is necessary. Deep learning based authentication algorithms have a number of applications in the said field. Adding multiple modalities makes the system more robust. In this research a joint multi-modal audio-visual deep learning based method has been devised to authenticate a person based on their voice as well as face. This two-step verification process works by learning face-feature based embeddings as well as voice-feature based embeddings to serve two purposes: 1) if the face presented matches with an identity in a reference database and 2) if the voice matches any voice in the reference database. This strategy can help prevent important systems from impostor attempts using modalities that are commonly present and available in consumer devices. 30cm.

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Audio Visual Authentication /

By Yousuf, Talha . . 52p. , In bio-related applications privacy is an essential element. While most of the techniques in Deep Learning rely on single modality, spoofing attacks can be minimized by em ploying multi-modal approaches. Purpose of this research is to develop a technique in which a person will be given some sentences to speak, audio-visual features will be merged and using this amalgam of both modalities, language model will validate if the text read actually validates against the passage given to read. This can be used as an authentication method to check if the user is actually live and hence can prevent the print attacks in case of mobile applications. 30cm.

Place Hold on Audio Visual Authentication /