Publication

Noisy Text Categorization

2004
Reports

Abstract

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 a recognition system). The performance of a categorization system over the clean and noisy (Word Error Rate between 10 and 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 and it is especially low for Recall values lower than 60 percent. New measures of the extraction process performance, allowing a better explanation of the categorization results, are proposed.

Official source

https://infoscience.epfl.ch/entities/publication/26528f36-94f9-46e4-bd12-2c00e69d3551

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2004
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/26528f36-94f9-46e4-bd12-2c00e69d3551

About this result

Related concepts (32)

Optical character recognition

Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of s of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).

Knowledge extraction

Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, s) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema.

Handwriting recognition

Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available.

Related publications (32)

Noisy Text Categorization

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Reduction of supervision for biomedical knowledge discovery

Measurement of transverse $\mathrm{\Lambda}$ and $\overline{\mathrm{\Lambda}}$ hyperon polarization in $p\mathrm{Pb}$ collisions at $\sqrt{{s}_{\mathrm{NN}}}=5.02\text{ }\text{ }\mathrm{TeV}$

Domain Adaptation and Investigation of Robustness of DNN-based Embeddings for Text-Independent Speaker Verification Using Dilated Residual Networks