Indexing and Retrieval of Digital Video Sequences based on Automatic Text Recognition

Efficient indexing and retrieval of digital video is an importantaspect of video databases. One powerful index for retrieval is the text appearing in them. It enables content- based browsing. We present our methods for automatic segmentation and recognition of text in digital videos. The algorithms we propose make use of typical characteristics of text in videos in order to enable and enhance segmentation and recognition performance. Especially the inter-frame dependencies of the characters provide new possibilities for their refinement. Then, a straightforward indexing and retrieval scheme is introduced. It is used in the experiments to demonstrate that the proposed text segmentation and text recognition algorithms are suitable for indexing and retrieval of relevant video scenes in and from a video data base. Our experimental results are very encouraging and suggest that these algorithms can be used in video retrieval applications as well as to recognize higher semantics in video

Automatic text segmentation and text recognition for video indexing

Efficient indexing and retrieval of digital video is an important function of video databases. One powerful index for retrieval is the text appearing in them. It enables content-based browsing. We present our methods for automatic seg-mentation of text in digital videos. The output is directly passed to a standard OCR software package in order to translate the segmented text into ASCII. The algorithms we propose make use of typical characteristics of text in videos in order to enable and enhance segmentation performance. Especially the inter-frame dependencies of the characters provide new possibilities for their refinement. Then, a straightforward indexing and retrieval scheme is intro-duced. It is used in the experiments to demonstrate that the proposed text segmentation algorithms together with exist-ing text recognition algorithms are suitable for indexing and retrieval of relevant video sequences in and from a video database. Our experimental results are very encouraging and suggest that these algorithms can be used in video retrieval applications as well as to recognize higher seman-tics in videos

Structuring lecture videos for distance learning applications. ISMSE

This paper presents an automatic and novel approach in structuring and indexing lecture videos for distance learning applications. By structuring video content, we can support both topic indexing and semantic querying of multimedia documents. In this paper, our aim is to link the discussion topics extracted from the electronic slides with their associated video and audio segments. Two major techniques in our proposed approach include video text analysis and speech recognition. Initially, a video is partitioned into shots based on slide transitions. For each shot, the embedded video texts are detected, reconstructed and segmented as high-resolution foreground texts for commercial OCR recognition. The recognized texts can then be matched with their associated slides for video indexing. Meanwhile, both phrases (title) and keywords (content) are also extracted from the electronic slides to spot the speech signals. The spotted phrases and keywords are further utilized as queries to retrieve the most similar slide for speech indexing. 1

Automated analysis and indexing of lecture videos

Learning from online videos mainly helps the students and every individual understand a specific topic easily because of the realistic picturization. One of resources available to students is automated analysis and indexing of online lecture videos using image processing. Many online educational organizations and universities use video lectures to support teaching and learning. In past decades, video lecture portals have been widely used and are very popular. The text displayed in these video lectures are a valuable source for analyzing and indexing the lecture contents. Considering this scenario, we present an approach for automatic analysis and indexing of lecture videos using OCR (Optical Character Recognition) technology. For this, we segregated the unique key frames from a lecture video to extract the video contents. After the segregation of key frames by applying OCR and ASR (Automatic Speech Recognition) technology we can extract the textual data contents from the video lecture. From the obtained metadata, we segmented the video lecture based on the time-based text occurrence of the topics. The performance and the effectiveness of proposed analysis and indexing is proven by the evaluation

Semantic keyword extraction via adaptive text binarization of unstructured unsourced video

We propose a fully automatic method for summarizing and indexing unstructured presentation videos based on text extracted from the projected slides. We use changes of text in the slides as a means to segment the video into semantic shots. Unlike precedent approaches, our method does not depend on availability of the electronic source of the slides, but rather extracts and recognizes the text directly from the video. Once text regions are detected within keyframes, a novel binarization algorithm, Local Adaptive Otsu (LOA), is employed to deal with the low quality of video scene text, before feeding the re-gions to the open source Tesseract1 OCR engine for recognition. We tested our system on a corpus of 8 presentation videos for a total of 1 hour and 45 minutes, achieving 0.5343 Precision and 0.7446 Recall Character recognition rates, and 0.4947 Precision and 0.6651 Recall Word recognition rates. Besides being used for multimedia documents, topic indexing, and cross referencing, our system can be integrated into summarization and presentation tools such as th

Language-based multimedia information retrieval

This paper describes various methods and approaches for language-based multimedia information retrieval, which have been developed in the projects POP-EYE and OLIVE and which will be developed further in the MUMIS project. All of these project aim at supporting automated indexing of video material by use of human language technologies. Thus, in contrast to image or sound-based retrieval methods, where both the query language and the indexing methods build on non-linguistic data, these methods attempt to exploit advanced text retrieval technologies for the retrieval of non-textual material. While POP-EYE was building on subtitles or captions as the prime language key for disclosing video fragments, OLIVE is making use of speech recognition to automatically derive transcriptions of the sound tracks, generating time-coded linguistic elements which then serve as the basis for text-based retrieval functionality

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

InfoLink: analysis of Dutch broadcast news and cross-media browsing

In this paper, a cross-media browsing demonstrator named InfoLink is described. InfoLink automatically links the content of Dutch broadcast news videos to related information sources in parallel collections containing text and/or video. Automatic segmentation, speech recognition and available meta-data are used to index and link items. The concept is visualised using SMIL-scripts for presenting the streaming broadcast news video and the information links