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With large databases of document images available,a method for users to find keywords in documents will be useful. One approach is to perform Optical Character Recognition (OCR) on each document followed by indexing of the resulting text. However, if the quality of the document is poor or time is critical,complete OCR
Training a bottleneck feature (BNF) extractor with multilingual data has been common in low resource keyword search. In a low resource application, the amount of transcribed target language data is limited while there are usually plenty of multilingual data. In this paper, we investigated two methods to train
This work demonstrates the development of Keyword Spotting (KWS) system using Vowel Onset Point (VOP), Vector Quantization (VQ) and Hidden Markov Model(HMM) based techniques. The goal of KWS system is to spot the keywords present in the test speech signal, while neglecting rest of the words. In this work, first
This paper proposes a novel system to automatically determine the sports type of a sports game by conducting keywords spotting on short fragments (around 10 minutes) of a sports game. In this system, we first develop an audio segmentation module as a front-end to separate announcers' speech efficiently from the
In this paper, a new information extraction system by statistical shallow parsing in unconstrained handwritten documents is introduced. Unlike classical approaches found in the literature as keyword spotting or full document recognition, our approach relies on a strong and powerful global handwriting model. A entire
recognition using audio and visual cues. The novelty lies in putting together the tasks such that they can provide relevant information to one another. We evaluate the performance of our system and present results for tasks such as keyword spotting and tracking re-identification on real-world meeting scenes collected in our
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