Evaluating NN and HMM Classifiers for Handwritten Word Recognition
نویسندگان
چکیده
This paper evaluates NN and HMM classifiers applied to the handwritten word recognition problem. The goal is analyse the individual and combined performance of these classifiers. They are evaluated considering two different combination strategies and the experiments are performed with the same database and similar feature sets. The strategy proposed takes advantage of the different but complementary mechanisms of NN and HMM to obtain a more efficient hybrid classifier. The recognition rates obtained vary from 75.9% using the HMM classifier alone to 90.4% considering the NN and HMM combination.
منابع مشابه
A hybrid large vocabulary handwritten word recognition system using neural networks with hidden Markov models
In this paper we present a hybrid recognition system that integrates hidden Markov models (HMM) with neural networks (NN) in a probabilistic framework. The input data is processed first by a lexicon–driven word recognizer based on HMMs to generate a list of the candidateN–best– scoring word hypotheses as well as the segmentation of such word hypotheses into characters. An NN classifier is used ...
متن کاملHolistic Farsi handwritten word recognition using gradient features
In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...
متن کاملOff-line Arabic Handwritten Recognition Using a Novel Hybrid HMM-DNN Model
In order to facilitate the entry of data into the computer and its digitalization, automatic recognition of printed texts and manuscripts is one of the considerable aid to many applications. Research on automatic document recognition started decades ago with the recognition of isolated digits and letters, and today, due to advancements in machine learning methods, efforts are being made to iden...
متن کاملOffline handwritten word recognition using a hybrid neural network and hidden Markov model
This paper describes an approach to combine neural network (NN) and Hidden Markov models (HMM) for solving handwritten word recognition problem. The preprocessing involves generating a segmentation graph that describes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each letter hypothesis in the segmentation graph. The HMM...
متن کاملComparison of Support Vector Machine and Neural Network in Character Level Discriminant Training for Online Word Recognition
Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. In the hybrid system, NN is used for character level recognition while HMM is used for producing word score based on the probability of the hypothesized characters combined. All reported results shows better recognition for the hybrid system due to better disc...
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 2002