Large Vocabulary Recognition of On - LineHandwritten Cursive
نویسندگان
چکیده
| This paper presents a writer independent system for large vocabulary recognition of on-line handwritten cursive words. The system rst uses a ltering module, based on simple letter features, to quickly reduce a large reference dictionary (lexicon) to a more manageable size; the reduced lexicon is subsequently fed to a recognition module. The recognition module uses a temporal representation of the input, instead of a static 2-dimensional image, thereby preserving the sequential nature of the data and enabling the use of a Time-Delay Neural Network (TDNN); such networks havee been previously successful in the continuous speech recognition domain. Explicit segmentation of the input words into characters is avoided by sequentially presenting the input word representation to the neural network-based recognizer. The outputs of the recognition module are collected and converted into a string of characters that is matched against the reduced lexicon using an extended Damerau-Levenshtein function. Trained on 2,443 unconstrained word images (11k characters) from 55 writers and using a 21k lexicon we reached a 97.9% and 82.4% top-5 word recognition rate on a writer-dependent and writer-independent test respectively.
منابع مشابه
NPen++: A Writer Independent, Large Vocabulary On-Line Cursive Handwriting Recognition System
متن کامل
Combination of multiple classifiers for handwritten word recognition
Because of large shape variations in human handwriting, recognition accuracy of cursive handwritten word is hardly satisfying using a single classifier. In this paper we introduce a framework to combine results of multiple classifiers and present an intuitive run-time weighted opinion pool (RWOP) combination approach for recognizing cursive handwritten words with a large size vocabulary. The in...
متن کاملOn-Line Handwriting Recognition Using Hidden Markov Models
New global information-bearing features improved the modeling of individual letters, thus diminishing the error rate of an HMM-based on-line cursive handwriting recognition system. This system also demonstrated the ability to recognize on-line cursive handwriting in real time. The BYBLOS continuous speech recognition system, a hidden Markov model (HMM) based recognition system, is applied to on...
متن کاملAn investigation of the use of trigraphs for large vocabulary cursive handwriting recognition
This paper presents an extensive investigation of the use of trigraphs for on-line cursive handwriting recognition based on Hidden Markov Models (HMMs). Trigraphs are context dependent HMMs representing a single written character in its left and right context, similar to triphones in speech recognition. Looking at the great success of triphones in continuous speech recognition ([1]-[3]), it was...
متن کاملAn Effective Character Separation Method for Online Cursive Uyghur Handwriting
There are many connected characters in cursive Uyghur handwriting, which makes the segmentation and recognition of Uyghur words very difficult. To enable large vocabulary Uyghur word recognition using character models, we propose a character separation method for over-segmentation in online cursive Uyghur handwriting. After removing delayed strokes from the handwritten words, potential breakpoi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1996