Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition

Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into informative signature feature maps using a sliding window-based method, successfully capturing the analytic and geometric properties of pen strokes with strong local invariance and robustness. A multi-spatial-context fully convolutional recurrent network (MCFCRN) is proposed to exploit the multiple spatial contexts from the signature feature maps and generate a prediction sequence while completely avoiding the difficult segmentation problem. Furthermore, an implicit language model is developed to make predictions based on semantic context within a predicting feature sequence, providing a new perspective for incorporating lexicon constraints and prior knowledge about a certain language in the recognition procedure. Experiments on two standard benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with correct rates of 97.10% and 97.15%, respectively, which are significantly better than the best result reported thus far in the literature.Comment: 14 pages, 9 figure

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Accuracy improvement in odia zip code recognition technique

Odia is a very popular language in India which is used by more than 45 million people worldwide, especially in the eastern region of India. The proposed recognition schemes for foreign languages such as Roman, Japanese, Chinese and Arabic can’t be applied directly for odia language because of the different structure of odia script. Hence, this report deals with the recognition of odia numerals with taking care of the varying style of handwriting. The main purpose is to apply the recognition scheme for zip code extraction and number plate recognition. Here, two methods “gradient and curvature method” and “box-method approach” are used to calculate the features of the preprocessed scanned image document. Features from both the methods are used to train the artificial neural network by taking a large no of samples from each numeral. Enough testing samples are used and results from both the features are compared. Principal component analysis has been applied to reduce the dimension of the feature vector so as to help further processing. The features from box-method of an unknown numeral are correlated with that of the standard numerals. While using neural networks, the average recognition accuracy using gradient and curvature features and box-method features are found to be 93.2 and 88.1 respectively

Recognition of handwritten Chinese characters by combining regularization, Fisher's discriminant and distorted sample generation

Proceedings of the 10th International Conference on Document Analysis and Recognition, 2009, p. 1026–1030The problem of offline handwritten Chinese character recognition has been extensively studied by many researchers and very high recognition rates have been reported. In this paper, we propose to further boost the recognition rate by incorporating a distortion model that artificially generates a huge number of virtual training samples from existing ones. We achieve a record high recognition rate of 99.46% on the ETL-9B database. Traditionally, when the dimension of the feature vector is high and the number of training samples is not sufficient, the remedies are to (i) regularize the class covariance matrices in the discriminant functions, (ii) employ Fisher's dimension reduction technique to reduce the feature dimension, and (iii) generate a huge number of virtual training samples from existing ones. The second contribution of this paper is the investigation of the relative effectiveness of these three methods for boosting the recognition rate. © 2009 IEEE.published_or_final_versio

Feature Extraction Methods for Character Recognition

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