1,856 research outputs found
Unconstrained Scene Text and Video Text Recognition for Arabic Script
Building robust recognizers for Arabic has always been challenging. We
demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid
architecture in recognizing Arabic text in videos and natural scenes. We
outperform previous state-of-the-art on two publicly available video text
datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a
new Arabic scene text dataset and establish baseline results. For scripts like
Arabic, a major challenge in developing robust recognizers is the lack of large
quantity of annotated data. We overcome this by synthesising millions of Arabic
text images from a large vocabulary of Arabic words and phrases. Our
implementation is built on top of the model introduced here [37] which is
proven quite effective for English scene text recognition. The model follows a
segmentation-free, sequence to sequence transcription approach. The network
transcribes a sequence of convolutional features from the input image to a
sequence of target labels. This does away with the need for segmenting input
image into constituent characters/glyphs, which is often difficult for Arabic
script. Further, the ability of RNNs to model contextual dependencies yields
superior recognition results.Comment: 5 page
Interactive searching and browsing of video archives: using text and using image matching
Over the last number of decades much research work has been done in the general area of video and audio analysis. Initially the applications driving this included capturing video in digital form and then being able to store, transmit
and render it, which involved a large effort to develop compression and encoding standards. The technology needed to do all this is now easily available and cheap, with applications of digital video processing now commonplace,
ranging from CCTV (Closed Circuit TV) for security, to home capture of broadcast TV on home DVRs for personal viewing.
One consequence of the development in technology for creating, storing and distributing digital video is that there has been a huge increase in the volume of digital video, and this in turn has created a need for techniques to allow effective management of this video, and by that we mean content management. In the BBC, for example, the archives department receives approximately 500,000 queries per year and has over 350,000 hours of content in its library. Having huge archives of video information is hardly any benefit if we have no effective means of being able to locate video clips which are of relevance to whatever our information needs may be. In this chapter we report our work on developing two specific retrieval and browsing tools for digital video information. Both of these are based on an analysis of the captured video for the purpose of automatically structuring into shots or higher level semantic units like TV news stories. Some also include analysis of the video for the automatic detection of features such as the presence or absence of faces. Both include some elements of searching, where a user specifies a query or information need, and browsing, where a user is allowed to browse through sets of retrieved video shots. We support the presentation of these tools with illustrations of actual video retrieval systems developed and working on hundreds of hours of video content
Character Recognition
Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field
Mathematical Methods Applied to Digital Image Processing
Introduction: Digital image processing (DIP) is an important research area since it spans a variety of applications. Although over the past few decades there has been a rapid rise in this field, there still remain issues to address. Examples include image coding, image restoration, 3D image processing, feature extraction and analysis, moving object detection, and face recognition. To deal with these issues, the use of sophisticated and robust mathematical algorithms plays a crucial role. The aim of this special issue is to provide an opportunity for researchers to publish their latest theoretical and technological achievements in mathematical methods and their various applications related to DIP. This special issue covers topics related to the development of mathematical methods and their applications. It has a total of twenty-four high-quality papers covering various important topics in DIP, including image preprocessing, image encoding/decoding, stereo image reconstruction, dimensionality and data size reduction, and applications
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