Vinciarelli A 2005 This work presents categorization experiments performed over noisy texts. By noisy, we mean any text obtained through an extraction process (affected by errors) from media other than digital texts (e.g., transcriptions of speech recordings extracted with a recognition system). The performance of a categorization system over the clean and noisy (Word Error Rate between approximately 10 and approximately 50 percent) versions of the same documents is compared. The noisy texts are obtained through handwriting recognition and simulation of optical character recognition. The results show that the performance loss is acceptable for Recall values up to 60-70 percent depending on the noise sources. New measures of the extraction process performance, allowing a better explanation of the categorization results, are proposed. https://sonar.ch/global/documents/145731 eng info:eu-repo/semantics/altIdentifier/doi/10.1109/TPAMI.2005.248 info:eu-repo/semantics/altIdentifier/pmid/16355657 IEEE transactions on pattern analysis and machine intelligence. - 2005 Algorithms Artificial Intelligence Documentation Electronic Data Processing Handwriting Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Models, Statistical Pattern Recognition, Automated Reading Stochastic Processes Noisy text categorization. http://purl.org/coar/resource_type/c_6501