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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 regions to the open source
find the reference sequence in long sequences and the accuracy of the determining sequence still need to improve. In this paper, a sliding window and the keyword tree based algorithm is employed to match the substring set of the sequence data and find the reference sequence with the greatest probability. The novel method
activations originating from different units. Having different regions being active depending on the input unit may help network to discriminate better and as a consequence yield lower error rates. This paper investigates stimulated training for automatic speech recognition of a number of languages representing different
This paper presents an automatic keyword extraction method from historical document images. The proposed method is language independent because it is purely appearance based, where neither lexical information nor any other statistical language models are required. Moreover, since it does not need word segmentation
image regions. The codebook is utilized to represent images by calculating the keyword statistics in the individual images as well as in the collection as a whole. To reduce the dimensionality of the sparse feature vector, latent semantic indexing technique is applied and a similarity matching function is proposed by
We propose the Bayesian Active Learning by Disagreement (BALD) model for keyword spotting in handwritten documents. In the context of keyword spotting in handwritten documents, the background text is all regions in the document that do not contain the keywords. The model tries to learn certain characteristics of the
The proliferation of geo-textual data gives prominence to spatial keyword search. The basic top-k spatial keyword query, returns k geo-textual objects that rank the highest according to their textual relevance and spatial proximity to query keywords and a query location. We define, study, and provide means of
aligning keywordregions with keyword-specific phone events for feature vector generation. Including such additional information by incorporating time margins can capture idiosyncratic pronunciation information and is shown to help our keyword-conditioned phonetic speaker verification system achieve more than 50% (relative
General public highly use keyword queries to fulfil their information needs on the Web. Semantic web aims at transforming the Web to a format which is machine readable. RDF is the common format used in the Semantic Web to store data. Several existing approaches have proposed methods for keyword query processing on RDF
forms are delivered to users for completion, storage and verification etc. In such situations these printed documents must return to digital form in order to participate in digitized workflows. In printed documents, highly heterogeneous contents of different regions and fields are present. They have different layout
retrieval scheme based on annotation keywords and visual content, which can benefit from the strength of text- and content-based retrieval. The system starts query triggered by some keywords, and further refines the retrieval result based on blobs and regions information. The first step is to complete semantic filtering with
This paper presents a new approach for text-based video content retrieval system. The proposed scheme consists of three main processes that are key frame extraction, text localization and keyword matching. For the key-frame extraction, we proposed a Maximally Stable Extremal Region (MSER) based feature which is
enhances the machine learning based Stanford CoreNLP Part-of-Speech (POS) tagger with the Twitter model to extract essential keywords from a tweet. The system was enhanced using two rule-based parsers and a corpus. The research was conducted using tweets of customer service requests sent to a telecommunication company. A
To create a better search experience for end users and to satisfy their actual intents even for vaguely formulated queries, a contemporary search engine has to go beyond simple keyword-based retrieval concepts. For a geospatial search, where user queries can be quite complex such as “places for winter sport holidays
Manual tagging has an important impact to performance of image/video searching by keyword. However, users usually mark tags only landmarks are as on only a few images in library and leave most contents untagged. If landmarks from different places are look alike, it is hard to distinguish even though surroundings are
Use of semantic content is one of the important tasks in image analysis, which needs to be addressed for improving image retrieval effectiveness. We present a method to assign multiple keywords to image using SVMs. Images are divided into three-level regions called global image, semi-global images and sub-images. For
To perform a semantic search on a large dataset of images, we need to be able to transform the visual content of images (colors, textures, shapes) into semantic information. This transformation, called image annotation, assigns a caption or keywords to the visual content in a digital image. In this paper we try to
In this paper, we propose a novel strategy at an abstract level by combining textual and visual clustering results to retrieve images using semantic keywords and auto-annotate images based on similarity with existing keywords. Our main hypothesis is that images that fall in to the same textcluster can be described
paper, we propose a Bayesian approach to region-based image annotation, which integrates the content-based search and context into a unified framework. The content-based search selects representative keywords by matching an unlabeled image with the labeled ones followed by a weighted keyword ranking, which are in turn used
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