ASM Based Synthesis of Handwritten Arabic Text Pages

Area of Science:

• Computer Science
• Artificial Intelligence
• Document Analysis

Background:

• Document analysis tasks like text recognition require extensive labeled datasets for training and validation.
• Generating such datasets, especially for Arabic handwriting, is labor-intensive, time-consuming, and often insufficient for research.
• This data scarcity hinders the development of effective preprocessing, segmentation, and recognition methods.

Purpose of the Study:

• To develop an efficient system for automatically generating synthetic Arabic handwritten document images and corresponding ground truth.
• To address the lack of sufficient natural datasets for training and validating Arabic handwriting recognition systems.
• To enable robust development of document analysis methods by providing a viable alternative to real-world data.

Main Methods:

• Utilized Active Shape Models (ASMs) trained on 28,046 online Arabic handwriting samples for character synthesis.
• Extracted statistical properties from the IESK-arDB database to simulate realistic writing variations like baselines, slant, and skew.
• Composed ASM-based character representations into words and text pages, applying B-Spline interpolation and rendering with consideration for writing speed and pen characteristics.

Main Results:

• Successfully generated synthetic Arabic Unicode text into realistic handwritten document images with detailed ground truth.
• Demonstrated the utility of the synthetic data by validating a segmentation method.
• Experimental comparisons with the IESK-arDB database showed promising results for training and testing document analysis methods on synthetic data.

Conclusions:

• The proposed system offers an efficient solution for creating synthetic Arabic handwritten data, mitigating the limitations of scarce real-world datasets.
• Synthetic data generated by this system can be effectively used to train and validate document analysis techniques, including segmentation.
• This approach encourages further research and development in Arabic handwriting recognition and other document analysis tasks where data availability is a bottleneck.