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This paper presents clustering experiments performed over noisy texts (i.e. texts that have been extracted through an automatic process like character or speech recognition). The effect of recognition errors is investigated by comparing clustering results ...
This work presents categorization experiments performed over noisy texts. By noisy it is meant any text obtained through an extraction process (affected by errors) from media other than digital texts (e.g. transcriptions of speech recordings extracted with ...
This paper presents experiments that evaluate the effect of different video segmentation methods on text-based video retrieval. Segmentations relying on modalities like speech, video and text or their combination are compared with a baseline sliding window ...
This work shows experiments on the retrieval of handwritten documents. The performance of the same state-of-the-art Information Retrieval system is compared when dealing with manual (no errors) and automatic (Word Error Rate around 50%) transcriptions of t ...
This work addresses the problem of reducing the time between query submission and results output in a retrieval system. The goal is achieved by considering only a database fraction as small as possible during the retrieval process. Our approach is based on ...
This work shows experiments on the retrieval of handwritten documents. The performance of the same state-of-the-art Information Retrieval system is compared when dealing with manual (no errors) and automatic (Word Error Rate around 50%) transcriptions of t ...
This paper presents a system for the offline recognition of cursive handwritten lines of text. The system is based on continuous density HMMs and Statistical Language Models. The system recognizes data produced by a single writer. No a-priori knowledge is ...
This work shows Information Retrieval experiments performed over handwritten documents produced by a single writer. The same retrieval task has been performed over both manual (no errors) and automatic (Word Error Rate around 45%) transcriptions of 200 han ...
This paper presents a system for the offline recognition of cursive handwritten lines of text. The system is based on continuous density HMMs and Statistical Language Models. The system recognizes data produced by a single writer. No a-priori knowledge is ...
This work presents a system for the categorization of noisy texts. By noisy it is meant any text obtained through an extraction process (affected by errors) from media different than digital texts. We show that, even with an average Word Error Rate of arou ...
