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Recognition Assistance Linguistic Feedback for Treating Errors of Recognition Processes

Recognition Assistance Linguistic Feedback for Treating Errors of Recognition Processes. Gábor Prószéky & Mátyás Naszódi. proszeky@morphologic.hu & naszodim@morphologic.hu. www. .hu. Rome, 21 May 2003. IKTA-063/2000 . Project Details. Consortium: MorphoLogic (100%)

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Recognition Assistance Linguistic Feedback for Treating Errors of Recognition Processes

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  1. Recognition AssistanceLinguistic Feedback for Treating Errors of Recognition Processes Gábor Prószéky & Mátyás Naszódi proszeky@morphologic.hu & naszodim@morphologic.hu www..hu Rome, 21 May 2003

  2. IKTA-063/2000 Project Details • Consortium:MorphoLogic (100%) • Project Timeframe: 1 Sept 2000 – 28 Feb 2002 (completed) • Total budget: 19,2 M HUF[approx. 76 000 €] • Funding received: 50% Rome, 21 May 2003

  3. IKTA-063/2000 Project Objectives • Support for three recognition processes: OCR, handwriting, speech • A new principle for generalized recognition assistance: an algorithm for blinking and attentive listening – backed by linguistic knowledge • Development of necessarytechnology • Prototype of the first application Rome, 21 May 2003

  4. IKTA-063/2000 ... this is the only way Why this way? OCR Unknown characters Input character graph Spellingchecker Statistical post-processor Combinedpost-processor comparator text Input Linguistic character graph Rome, 21 May 2003

  5. IKTA-063/2000 Features of the Prototype • equipped with a module forsegmentation of continuous input • handles incorrect data, in terms of timing and quality • applies linguistic modules in a parallel manner Rome, 21 May 2003

  6. IKTA-063/2000 Goals • better recognition quality for current (low noise) input types • a solution for recognizing noisy input • „reviving” the shrinking OCR market • trying a method that has a significant international impact Rome, 21 May 2003

  7. IKTA-063/2000 The Segmentation Module • simoultaneous operation (new!) of • (1) lexical analysis,(2) morphological analyisis,(3) handling syntactic patterns describing the error model • handling underspecified input: in terms of quality and timing (new!) Rome, 21 May 2003

  8. IKTA-063/2000 Some Characteristics of OCRed Hungarian Texts • average sentence length: 40 characters • average ambiguity: in every 3 characters • an average of 3 alternatives • that would require 1,500,000 tries • our ‘traditional’ analysis speed: 1000 words/sec • this means 20 hours of experimenting • t = c·elength (t – time, c – speed) Rome, 21 May 2003

  9. IKTA-063/2000 Instead: The FSA Used in the Project • finite state automaton: 100,000 words/sec • we ask the system whether the input is a prefix of something known or not (can it be continued?) • Recognition time:‘traditional’ analysis: t = c·elengthFSA: t = c·length·log(length) Rome, 21 May 2003

  10. IKTA-063/2000 Handling OCR Alternatives Rome, 21 May 2003

  11. IKTA-063/2000 Handling phonetic alternatives OM, 2002. június 12.

  12. ... every project and every presentation must come to an end …

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