Introduction
Handwriting, a fundamental human activity, has been the cornerstone of information recording for centuries, deeply ingrained in early education and characterized by its emphasis on accuracy. With the advent of personal mobile devices featuring touchscreens and styluses, the use of handwriting interfaces, akin to traditional pencil and paper, has surged in popularity. These interfaces offer a natural and versatile experience for creating documents that encompass a range of elements, including text, mathematical expressions, diagrams, tables, and images.
Despite the proliferation of these interfaces, the visual quality of handwritten content often falls short of professional standards, posing a challenge for clarity and readability. This paper presents two approaches aimed at enhancing the legibility of handwritten text by straightening the content: a recognition-based method and a recognition-independent approach, specifically a Hierarchical Recurrent Neural Network (Hierarchical RNN) method. The goal is to create a lightweight, resource-efficient solution for low-end mobile devices that can accommodate various writing styles without altering the original style or readability.
Proposed Approaches
A. Recognition-Based Method
The recognition-based method for enhancing handwriting legibility involves three stages:
- Hierarchical Neural Network Analysis: This stage employs a hierarchical neural network to analyze input strokes and identify text blocks and lines.
- Symbol Recognition: A Bidirectional Long Short-Term Memory (BLSTM) neural network is utilized to recognize symbols and their corresponding points, taking into account their position relative to text metrics such as baseline position, ascent, x-height, and descent.
- Structural Analysis: This final stage conducts a detailed structural analysis to determine precise character positioning.
The alignment system places each symbol on a straight baseline, considering text metrics for alignment. Symbols are classified into levels (‘Tall’, ‘Basic’, ‘Lengthy’, ‘Top’, ‘Bottom’, ‘Middle’) based on their expected vertical locations. Complex geometric feature analysis aids in determining baselines, especially for symbols with diverse writing styles. Continuously written symbols are grouped and aligned to maintain the original handwriting sequence. The final step calculates text line metrics for alignment, adjusting strokes to fit within designated baselines while ensuring consistent line spacing.
B. Recognition-Independent Method
Inspired by a simple encoder-decoder framework, the recognition-independent method offers an alternative approach to enhancing handwriting legibility. This method focuses on the intrinsic properties of handwriting, rather than relying on character recognition, to achieve alignment and legibility improvements.
Conclusion
Both proposed methods aim to refine the visual representation of handwritten content, making it more legible and aesthetically pleasing. The recognition-based approach leverages character recognition to enhance legibility, while the recognition-independent method focuses on the intrinsic properties of handwriting. These approaches are designed to be lightweight and resource-efficient, making them suitable for on-device implementation on low-end mobile devices, catering to the diverse needs of users in terms of writing styles and preferences.
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