caa a22 4500 445379634 CHVBK 20180317143013.0 cr unu---uuuuu 170323e20110601xx s 000 0 eng 10.1007/s10032-010-0139-z doi (NATIONALLICENCE)springer-10.1007/s10032-010-0139-z Unconstrained handwritten document retrieval [Elektronische Daten] [Huaigu Cao, Venu Govindaraju, Anurag Bhardwaj] With the ever-increasing growth of the World Wide Web, there is an urgent need for an efficient information retrieval system that can search and retrieve handwritten documents when presented with user queries. However, unconstrained handwriting recognition remains a challenging task with inadequate performance thus proving to be a major hurdle in providing robust search experience in handwritten documents. In this paper, we describe our recent research with focus on information retrieval from noisy text derived from imperfect handwriting recognizers. First, we describe a novel term frequency estimation technique incorporating the word segmentation information inside the retrieval framework to improve the overall system performance. Second, we outline a taxonomy of different techniques used for addressing the noisy text retrieval task. The first method uses a novel bootstrapping mechanism to refine the OCR'ed text and uses the cleaned text for retrieval. The second method uses the uncorrected or raw OCR'ed text but modifies the standard vector space model for handling noisy text issues. The third method employs robust image features to index the documents instead of using noisy OCR'ed text. We describe these techniques in detail and also discuss their performance measures using standard IR evaluation metrics. Springer-Verlag, 2010 Cao Huaigu Raytheon BBN Technologies, 02138, Cambridge, MA, USA aut Govindaraju Venu Department of Computer Science and Engineering, University at Buffalo, 14260, Amherst, NY, USA aut Bhardwaj Anurag Department of Computer Science and Engineering, University at Buffalo, 14260, Amherst, NY, USA aut International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/2(2011-06-01), 145-157 1433-2833 14:2<145 2011 14 10032 https://doi.org/10.1007/s10032-010-0139-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10032-010-0139-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Cao Huaigu Raytheon BBN Technologies, 02138, Cambridge, MA, USA aut NATIONALLICENCE 700 1- Govindaraju Venu Department of Computer Science and Engineering, University at Buffalo, 14260, Amherst, NY, USA aut NATIONALLICENCE 700 1- Bhardwaj Anurag Department of Computer Science and Engineering, University at Buffalo, 14260, Amherst, NY, USA aut NATIONALLICENCE 773 0- International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/2(2011-06-01), 145-157 1433-2833 14:2<145 2011 14 10032 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer