Overview of Handwritten Optical Character Recognition (OCR)
The paper "Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)" provides a systematic overview of the advancements in techniques for recognizing handwritten characters and symbols using OCR systems over the period spanning from 2000 to 2018. Handwritten OCR holds substantial potential in digitizing handwritten manuscripts and documents, which are heavily utilized in various sectors such as historical data preservation, legal documentation, and educational settings.
This review collated and examined 142 articles from established electronic databases, following a structured review protocol. The focus was to capture the evolution and state-of-the-art techniques in feature extraction, classification methodologies, and highlight critical research gaps in different languages including English, Arabic, Indian scripts, Chinese, Urdu, and Persian.
Review Methodology and Data Analysis
The review utilized a systematic approach based on the guidelines by Kitchenham et al., ensuring a comprehensive protocol to extract relevant literature. Studies were meticulously selected based on predefined inclusion and exclusion criteria, yielding a final dataset of 142 research articles out of an initial pool of 954. The synthesis of the literature involved exploring feature extraction techniques, classification methods, and dataset availability relevant to OCR.
State-of-the-Art Techniques in Handwritten OCR
The review identifies several techniques employed in OCR, with early systems relying significantly on feature extraction and classification algorithms. Here's a summary of the key methodologies:
- Artificial Neural Networks (ANN): Early applications leveraged multi-layer perceptrons (MLP) and later advanced to CNNs, particularly due to CNN's robust performance in image-based recognition tasks. The transition to CNN from MLP and other architectures has been driven by enhanced accuracy in recognizing nuanced and variable input patterns in handwritten documents.
- Support Vector Machines (SVM): Widely utilized prior to the advent of deep learning, SVMs served as a reliable tool for classification. Its applications spanned several languages, providing a solid baseline for character recognition before the efficiency of CNNs was established.
- Statistical Methods: Hidden Markov Models (HMM) and k-nearest neighbors (kNN) were commonly employed, leveraging probabilistic models and non-parametric approaches for capturing variations in handwriting.
- Template Matching and Structural Pattern Recognition: These techniques emphasize recognizing patterns through predefined templates or employing graph-based models to capture the structural aspects of characters.
Comparative Analysis of Datasets and Languages
The availability of datasets plays a crucial role in the development and testing of OCR systems. Key datasets like MNIST, IAM, and CEDAR have been pivotal for English script recognition, while IFN/ENIT and UCOM datasets cater to Arabic and Urdu languages, respectively. Each dataset's quality significantly impacts the generalizability and real-world application of the developed OCR systems.
The paper highlights a significant concentration of research and datasets available for globally prominent languages such as English and Arabic, contrasted with a gap in resources for lesser-studied and endangered languages. This disparity calls for a concerted effort in data collection and OCR system development for a broader array of linguistic scripts.
Trends and Future Directions
The shift towards deep learning, specifically CNNs, marks a notable trend in recent OCR systems, significantly contributing to performance improvements across diverse languages. Future research opportunities lie in extending OCR capabilities to multilingual and document-in-the-wild scenarios, bridging the gap between laboratory achievements and practical applications.
Furthermore, as the demand for digitizing handwritten resources grows, future research can explore creating comprehensive datasets for low-resource languages, potentially preserving cultural heritage. The paper encourages exploring commercial viability and deploying cost-effective OCR solutions in real-world settings.
In conclusion, while this systematic review captures the progress and challenges in handwritten OCR, it also sets a foundation for addressing existing gaps and steering future research toward more inclusive and extensive OCR capabilities.