Handwritten Text Recognition Using Machine Learning
Kartik Saini, Khushi Sharma, Akshaj Agarwal, Kishan Jayan, Deepali Dev · 2023
Handwritten text recognition is a difficult task with numerous applications in document analysis, postal automation, and historical document preservation. There has been substantial progress in handwritten text recognition methods in recent years, ranging from traditional machine learning-based approaches to deep learning-based methods. This survey paper provides an in-depth look at the current methods for recognizing handwritten text, with an emphasis on the most recent and promising techniques. The paper discusses a variety of topics, such as data pre-processing, feature extraction, model selection, and model fine-tuning. On benchmark datasets, the performance of various methods is evaluated and compared, and the strengths and weaknesses of each approach are discussed. In addition, the survey highlights current research challenges and future directions in the field of handwritten text recognition. The purpose of this paper is to provide a useful resource for researchers and practitioners working in this field, as well as to facilitate further progress in the development of robust and accurate handwritten text recognition systems. Aside from the foregoing, this survey paper delves into the various handwritten text recognition tasks, such as offline and online recognition, as well as the various types of handwriting, such as cursive, print, and mixed script.