Proceedings Article | 9 October 2024
KEYWORDS: Optical character recognition, Deep learning, Image segmentation, Image processing, Pattern recognition, Object detection, Data modeling, Analytical research
Text recognition for Tibetan historical document images is the automated process of extracting and identifying text from these photos utilizing image processing, computer vision, and natural language processing techniques. It is generally understood to be a method of extracting text from visual data. In order to create editable, searchable, and analytical text forms that readers, researchers, and academics may study, preserve, and pass on, it attempts to analyze, identify, and change the text found in traditional Tibetan historical document images. Recent years have seen significant advancements in the effectiveness and performance of Tibetan historical document image and text identification thanks to the quick development of artificial intelligence technology. However, issues with low image quality, non-standard formatting, and notable font style inconsistencies persist in the field of image recognition of Tibetan historical document texts. As a result, the accuracy and universality of current recognizers are low. First, this article gives a general overview of the basic knowledge of Tibetan historical documents, which are the research objects in this field. This helps to better summarize the work of predecessors and support future research.The methods of integrating deep learning and conventional methods for Tibetan historical document image text recognition were then arranged, categorized, summarized, and introduced around the four subtasks of dataset construction, text detection, text recognition, and layout analysis. This served as the basis for sorting through the pertinent statistics and assessment indicators and summarizing the state and advancement of the research. Lastly, future technical development patterns are projected based on the primary issues and problems in this subject that require immediate attention.
