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 (MC-FCRN) 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.

Advanced Sequence Classification Techniques Applied to Online Handwriting Recognition 高识别率的在线手写英文字符识别

手写的字符识别,用C++语言实现,可以识别数字,字母,识别率高

该课题为基于MATLAB光流法OCR的手写数字识别系统。带一个GUI界面。可以识别单独字符，也可以识别连续的字符串。

基于opencv的手写数字字符识别，里边附加解释文档和流程图，代码附有注释

原理简单介绍：使用OpenCV中svm训练英文字符图片，进而识别。 资源包括完整代码，训练图像，测试图片

本文为opencv 基于knn的手写数字字符识别,开发环境为vs2013+opencv2.4.13，欢迎下载学习

实现了基于opencv 的手写数字字符识别 主要参照文章： http://blog.damiles.com/2008/11/basic-ocr-in-opencv/ 基本上就是按着人家的代码来配置的，完后小改动了几个参数，写了一个文档，方便大家学习吧。