Study on Automated Approach to Recognize Characters for Handwritten and Historical Document

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives. In this study, we introduce a hybrid form archive written in French: the Belfort civil registers of births. The digitization of these historical documents is challenging due to their unique characteristics such as writing style variations, overlapped characters and words, and marginal annotations. The objective of this survey paper is to summarize research on handwritten text documents and provide research directions toward effectively transcribing this French dataset. To achieve this goal, we presented a brief survey of several modern and historical HTR offline systems of different international languages, and the top state-of-the-art contributions reported of the French language specifically. The survey classifies the HTR systems based on techniques employed, datasets used, publication years, and the level of recognition. Furthermore, an analysis of the systems' accuracies is presented, highlighting the best-performing approach. We have also showcased the performance of some HTR commercial systems. In addition, this paper presents a summarization of the HTR datasets that publicly available, especially those identified as benchmark datasets in the International Conference on Document Analysis and Recognition (ICDAR) and the International Conference on Frontiers in Handwriting Recognition (ICFHR) competitions. This paper, therefore, presents updated state-of-the-art research in HTR and highlights new directions in the research field.

An Effective Learning Evaluation Method Based on Text Data with Real-time Attribution - A Case Study for Mathematical Class with Students of Junior Middle School in China

In today's intelligent age, the vigorous development of education-based information analysis technology has had a profound impact on the education and teaching process. The use of computational linguistics technology to extract teaching data for learning evaluation is an important hot domain in this research field. Therefore, the study of student learning assessment method based on text data has become a key issue. The text data extracted from the education process has attributes related to time and operational attributes, which are important indicators to measure the effect of student learning effect. However, these attributes are not focused by the traditional educational effect evaluation method, which make the learning effect of students is difficult to measure comprehensively and effectively. In response to this problem, this article first uses perception technology to extract learning text data based on time and operational attributes. Secondly, according to the real-time attributes of text data, such as time and operation attributes, a learning evaluation method based on real-time text data is proposed. Finally, this article compares the traditional evaluation method with the proposed method. The results show that using real-time attribute text data is more effective in students’ learning measure.