Handwritten Chinese character recognition using fuzzy image alignment

Abstract

The task of handwritten Chinese character recognition is one of the most challenging areas of human handwriting classification. The main reason for this is related to the writing system itself which encompasses thousands of characters, coupled with high levels of diversity in personal writing styles and attributes. Much of the existing work for both online and off-line handwritten Chinese character recognition has focused on methods which employ feature extraction and segmentation steps. The preprocessed data from these steps form the basis for the subsequent classification and recognition phases. This paper proposes an approach for handwritten Chinese character recognition and classification using only an image alignment technique and does not require the aforementioned steps. Rather than extracting features from the image, which often means building models from very large training data, the proposed method instead uses the mean image transformations as a basis for model building. Northwestern Polytechnical University, Xi'an 710072, China no subjective tuning of the feature extraction is required. In addition by employing a fuzzy-entropy-based metric, the work also entails improved ability to model different types of uncertainty. The classifier is a simple distance-based nearest neighbour classification system based on template matching. The approach is applied to a publicly available real-world database of handwritten Chinese characters and demonstrates that it can achieve high classification accuracy and is robust in the presence of noise.

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

“…A handwritten Chinese character recognition system based on image alignment technology was proposed by Li et al [71] in 2016. The algorithm builds a nearest neighbor classifier based on template matching and improves the modeling ability of different text types by utilizing the average image transformation as a basic module, and then adopts a fuzzy entropy-based metric function.…”

Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

“…Modeling this uncertainty, thus, seems reasonable during the process of motion estimation in order to reliably quantify functional characteristics of the heart. A suitable framework for performing this task is 'fuzzy theory' which can handle both intrinsic and extrinsic uncertainties in such a process and has a wide range of applications in different image processing fields including feature extraction [3]- [5], image thresholding [6], image segmentation [7], curve alignment [8], [9], motion estimation [10] and point-set matching [11]- [14]. Considering the potential of the fuzzy theory and the uncertainties (i.e., fuzziness) associated with the 3D TTE cardiac motion estimation, a fuzzy non-rigid registration algorithm is proposed for efficient quantification of cardiac function.…”

Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets

“…Ran et al [10] proposed a normalized overlapped fuzzy bielastic grid, which was used to improve the effectiveness of the proposed features. Li et al [11] used fuzzy entropy to classify Chinese characters with high accuracy and improved the recognition rate of Chinese characters. Liu and Meng [12] improved the membership function of a fuzzy support vector machine and improved the ability of text classification and recognition efficiency.…”