A Deep Reinforcement Learning-based Study on Handwriting Difficulty Analysis
- Publisher:
- World Scientific
- Publication Type:
- Chapter
- Citation:
- Advances in Pattern Recognition and Artificial Intelligence, 2022, 06, pp. 97-117
- Issue Date:
- 2022-11-30
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 20232608_9962839250005671.pdf | Published version | 612.62 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
A writing system contains several patterns/graphical symbols, which represent linguistic constructs. Such patterns/symbols are orthographically related to the phonology of a spoken language. A writing system may contain a few thousands of such symbols (e.g., about 10,000 frequently-used symbols for the Chinese writing system, about 1000 for Devanagari, etc.). Therefore, a section of people face difficulties in learning and writing all the character structures of a writing system. Besides, due to the dominance of electronic typing in this digital age, individuals often experience increasing difficulty in writing the characters by hand, even though they can read those. In this chapter, we analyze the handwriting of an individual and attempt to understand which character shapes are challenging for that person to write. We propose here a deep reinforcement learning-based model to predict the difficulty measure/score of a character written by an individual. For experimentation, we have chosen Bengali, a script of the Indian subcontinent, which contains about 1000 complex character shapes. We have obtained some promising results from our experiments.
Please use this identifier to cite or link to this item: