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Improving offline handwritten text recognition with hybrid HMM/ANN models
Salvador España-Boquera1, Maria Jose Castro-Bleda, Jorge Gorbe-Moya
1Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain. sespana@dsic.upv.es
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 18, 2010
Summary
This study introduces hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for improved unconstrained offline handwritten text recognition. New preprocessing techniques enhance accuracy by normalizing text size and correcting slope and slant.
Area of Science:
- Computer Science
- Artificial Intelligence
- Pattern Recognition
Background:
- Handwritten text recognition is challenging due to variations in writing styles.
- Existing methods often struggle with unconstrained and offline data.
- Preprocessing is crucial for improving recognition accuracy.
Purpose of the Study:
- To propose and evaluate hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for unconstrained offline handwritten text recognition.
- To introduce novel supervised learning techniques for text image preprocessing.
- To achieve state-of-the-art recognition rates on benchmark datasets.
Main Methods:
- Hybrid HMM/ANN models combining Markov chains for structure and Multilayer Perceptrons for emission probabilities.
- Supervised learning methods for slope correction and size normalization using text contour extrema.
- Non-uniform slant removal using Artificial Neural Networks.
Main Results:
- The proposed hybrid models demonstrate high performance on offline handwritten text lines.
- Preprocessing techniques effectively remove slope, slant, and normalize text size.
- Achieved recognition rates are competitive with, and among the best reported in, the literature.
Conclusions:
- Hybrid HMM/ANN models offer a powerful approach for handwritten text recognition.
- Advanced preprocessing significantly boosts recognition accuracy.
- The developed methods represent a substantial advancement in the field.