- The paper introduces a novel diagonal-based feature extraction method for offline handwritten alphabet recognition using a multilayer feed-forward neural network.
- The method extracts features by tracing diagonals within image zones, achieving high recognition rates of 97.8% with 54 features and 98.5% with 69 features compared to horizontal/vertical methods.
- This diagonal-based feature extraction method shows superiority over traditional techniques, offering practical implications for document digitization and other character recognition applications.
Diagonal-Based Feature Extraction for Handwritten Alphabets Recognition Using Neural Networks
The paper presents an innovative approach to offline handwritten alphabetical character recognition employing a multilayer feed-forward neural network. The focus is on a novel feature extraction methodology termed diagonal-based feature extraction, which sets it apart from traditional horizontal and vertical extraction techniques. With an emphasis on optimizing recognition accuracy and reducing computational expense, this research provides significant contributions to the field of OCR in offline handwriting.
The core advancement in this paper lies in the diagonal feature extraction mechanism. Characters are resized into 90x60 pixel images and further divided into 54 zones, each zone being 10x10 pixels. The technique involves tracing diagonals within these zones, aggregating pixel values to generate 54 primary features. This is complemented by incorporating additional row-wise and column-wise averaging, generating an augmented feature set of 69 attributes per character. Such a comprehensive approach ensures that the feature set thoroughly represents different aspects of character shapes, thereby enhancing recognition potential.
Neural Network Architecture
The paper details the utilization of a feed-forward back propagation neural network for the classification task. This architecture, comprising two hidden layers, features an input size in accordance with the feature vector length—54 or 69—tailored for each experimental setup. A competitive layer constitutes the output, designed to discern characters based on an exhaustive training regimen. Noteworthy is the application of a gradient descent with momentum and adaptive learning rates, which underpin efficient convergence without significant overfitting.
Experimental Validation and Results
This study provides a comparative analysis of three different feature extraction paths: horizontal, vertical, and diagonal. Results illustrate that the diagonal-based methodology substantially outperforms its counterparts, securing recognition rates of 97.8% with 54 features and 98.5% with 69 features. This enhanced accuracy underscores the efficacy of diagonal features in capturing unique character intricacies that are less discernible through conventional methods.
Implications and Future Work
The findings advocate for diagonal feature extraction's superiority in character recognition tasks, which holds practical implications for numerous applications such as document digitization, automated name recognition, and postal address processing. By demonstrating a remarkably high accuracy rate coupled with efficient training, the research sets a benchmark in offline character recognition that could be adapted to multilingual character sets and larger datasets.
Theoretically, this study prompts further exploration of hybrid feature extraction techniques combining diagonal methods with other advanced approaches like machine learning-inspired pattern replication. Future developments might include integration with dynamic neural network architectures, such as convolutional neural networks, which could refine the recognition task further, addressing complex scripts or illegible handwriting more effectively.
Considering these findings, the paper solidifies the position of diagonal-based feature extraction as a critical component in the evolving landscape of image processing and pattern recognition, highlighting its potential in broadening the horizons of efficient handwritten text recognition systems.