In recent years, machine learning has emerged as a powerful tool for various applications in the field of natural language processing and character recognition. This paper presents an in-depth exploration of the application of machine learning techniques for Japanese Kanji detection. Kanji which is an integral part of the Japanese writing system, poses unique challenges due to its complexity and the vast number of characters involved. The ability to accurately detect and classify Kanji characters has significant implications for a wide range of applications, including optical character recognition (OCR), language processing, Image-text detection and conversion and information retrieval. This study investigates the effectiveness of the machine learningalgorithm, Convolutional neural networks (CNNs)with conjoining help from previous work which included neural machine translation (NMT), recurrent neural networks (RNNs), and support vector machines (SVMs), in the task of Kanji detection.

Various pre-processing techniques such as loading, grayscale conversion, thresholding, contour detection, bounding boxes, and extraction of characters were utilised to enhance the model’s performance.

Additionally,a dataset comprising a diverse set of extracted handwritten Kanji characters was used for training and validation. The primary research aim and objective is to explore the potential of machine learning, deep learning, and artificial intelligence in facilitating the detection of Japanese Kanji characters. Furthermore, we delve into the challenges of handling class imbalance issues, where some Kanji characters are significantly more common than others, and propose strategies to mitigate this problem. The experimental results demonstrate the potential of machine learning in accurately detecting Japanese Kanji characters, achieving high precision and recall rates. The findings have practical implications for improving the accuracy and efficiency of OCR systems for Japanese text and contribute to the broader field of character recognition. In conclusion, this paper provides valuable insights into the application of machine learning for Japanese Kanji detection. The research contributes to the growing body of knowledge in character recognition and paves the way for enhanced Kanji-related applications in the realms of language processing, document analysis, and information retrieval.