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Parallel letter recognition is the most widely accepted model of word recognition by psychologists today. [3] In this model, all letters within a group are perceived simultaneously for word recognition. In contrast, the serial recognition model proposes that letters are recognized individually, one by one, before being integrated for word ...
N is the number of words in the reference (N=S+D+C) ... When reporting the performance of a speech recognition system, sometimes word accuracy (WAcc) is used instead:
Letter frequency is the number of times ... on words that appear 100,000 times or more in Google Books data transcribed using optical character recognition ...
Participants are given pairs, usually of words, A1-B1, A2-B2...An-Bn (n is the number of pairs in a list) to study. ... Memory word recognition also improved ...
Intelligent Word Recognition, or IWR, [1] is the recognition of unconstrained handwritten words. [2] IWR recognizes entire handwritten words or phrases instead of character-by-character, like its predecessor, optical character recognition (OCR). [ 3 ]
Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words. In particular, words which appear in similar contexts are mapped to vectors which are nearby as measured by cosine similarity .
The logogen model of 1969 is a model of speech recognition that uses units called "logogens" to explain how humans comprehend spoken or written words. Logogens are a vast number of specialized recognition units, each able to recognize one specific word. This model provides for the effects of context on word recognition.
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.