Extracting textual overlays from social media videos using neural networks

Textual overlays are often used in social media videos as people who watch them without the sound would otherwise miss essential information conveyed in the audio stream. This is why extraction of those overlays can serve as an important meta-data source, e.g. for content classification or retrieval tasks. In this work, we present a robust method for extracting textual overlays from videos that builds up on multiple neural network architectures. The proposed solution relies on several processing steps: keyframe extraction, text detection and text recognition. The main component of our system, i.e. the text recognition module, is inspired by a convolutional recurrent neural network architecture and we improve its performance using synthetically generated dataset of over 600,000 images with text prepared by authors specifically for this task. We also develop a filtering method that reduces the amount of overlapping text phrases using Levenshtein distance and further boosts system's performance. The final accuracy of our solution reaches over 80A% and is au pair with state-of-the-art methods.Comment: International Conference on Computer Vision and Graphics (ICCVG) 201

Simulasi Teknik Modulasi Ofdm Qpsk Dengan Menggunakan Matlab

This paper provides a brief explanation of the processing steps involved in Orthogonal Frequency Division Multiplexing (OFDM) with Quadrature Phase Shift Keying (QPSK) modulation technique implemented as a simulation program in MatLab. Input data of the simulation program in the form of random bit stream or text can be selected by users. The process conducted in the simulation is divided into three consecutive steps, processes in the OFDM transmitter, in transmission channel and in the OFDM receiver. The result of computation and the wave form involved in every processing steps will be displayed during the simulation. The final result shows, how the input data go through processing steps in the OFDM transmitter, transmission channel with noise and finally received by OFDM receiver will produce the output, random bit stream or text, exactly similar with input dat

Parallelization and Optimization of Image Processing Applications

This Bachelor's Thesis was performed during a study stay at the École Supérieure d'Ingénieurs en Électronique et Électrotechnique Paris, France. It proposes solution for speeding up image processing algorithm and its adoption for use with real-time video stream from the infra red camera. The first part discusses characteristics and basic principles of the IR technology, followed by specifications of used camera. Ongoing text also proposes solution of problems concerning network communication with the camera. In addition, it describes camera's output stream format characteristics and solution for output visualisation. Substantial part of this work covers issues concerning parallelization and optimization of video stream and image file data processing. Problem of the parallelisation for this case is explained together with implemented parallelization method. Entire theoretical part is supported with the real results, benchmarks, which are presented in the last chapter.This Bachelor's Thesis was performed during a study stay at the École Supérieure d'Ingénieurs en Électronique et Électrotechnique Paris, France. It proposes solution for speeding up image processing algorithm and its adoption for use with real-time video stream from the infra red camera. The first part discusses characteristics and basic principles of the IR technology, followed by specifications of used camera. Ongoing text also proposes solution of problems concerning network communication with the camera. In addition, it describes camera's output stream format characteristics and solution for output visualisation. Substantial part of this work covers issues concerning parallelization and optimization of video stream and image file data processing. Problem of the parallelisation for this case is explained together with implemented parallelization method. Entire theoretical part is supported with the real results, benchmarks, which are presented in the last chapter.

Background Knowledge Based Multi-Stream Neural Network for Text Classification

As a foundation and typical task in natural language processing, text classification has been widely applied in many fields. However, as the basis of text classification, most existing corpus are imbalanced and often result in the classifier tending its performance to those categories with more texts. In this paper, we propose a background knowledge based multi-stream neural network to make up for the imbalance or insufficient information caused by the limitations of training corpus. The multi-stream network mainly consists of the basal stream, which retained original sequence information, and background knowledge based streams. Background knowledge is composed of keywords and co-occurred words which are extracted from external corpus. Background knowledge based streams are devoted to realizing supplemental information and reinforce basal stream. To better fuse the features extracted from different streams, early-fusion and two after-fusion strategies are employed. According to the results obtained from both Chinese corpus and English corpus, it is demonstrated that the proposed background knowledge based multi-stream neural network performs well in classification tasks

Topic detection and tracking using hidden Markov models

There is a continuous progress in automatic recording of broadcast speech using speech recognition. With the increasing use of this technology, a new source of data is added to the pool of information available over web. This has increased the need to categorize the resulting text, based on their topic for the purpose of information retrieval. In this thesis we present an approach to automatically assign a topic or track a change of topic in a stream of input data. Our approach is based on the use of Hidden Markov Models and language processing techniques. We consider input text as stream of words and use Hidden Markov Model to assign the most appropriate topic to the text. Then we process this output to identify the topic boundaries. The main focus of this thesis is to automatically assign a topic to specific story

Моделі обробки потоків текстових даних в рушії Apache Spark Structured Streaming

В даній статті розглядається рушій обробки потоків текстових даних Apache Spark Structured Streaming, описується його принцип роботи та складові. Також розглядаються моделі обробки потоків текстових даних, їх реалізації в рушії Spark Structured Streaming, порівнюються методи мікро-пакетної та потокової обробки, та описуються переваги й недоліки кожного з них.This paper discusses the Apache Spark Structured Streaming text data stream processing engine, describes its working principle and components. Models of text data stream processing, their implementation in the Spark Structured Streaming engine are also considered, micro-batch and stream processing methods are compared, and the advantages and disadvantages of each of them are described

Information filtering in high velocity text streams using limited memory - An event-driven approach to text stream analysis

This dissertation is concerned with the processing of high velocity text streams using event processing means. It comprises a scientific approach for combining the area of information filtering and event processing. In order to be able to process text streams within event driven means, an event reference model was developed that allows for the conversion of unstructured or semi-structured text streams into discrete event types on which event processing engines can operate. Additionally, a set of essential reference processes in the domain of information filtering and text stream analysis were described using event-driven concepts. In a second step, a reference architecture was designed that described essential architectural components required for the design of information ltering and text stream analysis systems in an event-driven manner. Further to this, a set of architectural patterns for building event driven text analysis systems was derived that support the design and implementation of such systems. Subsequently, a prototype was built using the theoretic foundations. This system was initially used to study the effect of sliding window sizes on the properties of dynamic sub-corpora. It could be shown that small sliding window based corpora are similar to larger sliding windows and thus can be used as a resource-saving alternative. Next, a study of several linguistic aspects of text streams was undertaken that showed that event stream summary statistics can provide interesting insights into the characteristics of high velocity text streams. Finally, four essential information filtering and text stream analysis components were studied, viz. filter policies, term weighting, thresholds and query expansion. These were studied using three temporal search profile types and were evaluated using standard information retrieval performance measures. The goal was to study the efficiency of traditional as well as new algorithms within the given context of high velocity text stream data, in order to provide advise which methods work best. The results of this dissertation are intended to provide software architects and developers with valuable information for the design and implementation of event-driven text stream analysis systems