一种基于前端预测识别的粘连字符分割方法

在字符识别领域，对粘连字符的识别是一个被广泛关注的技术难点，未能准确分割粘连字符是产生识别错误的主要原因之一。在总结已有方法的特点及不足的基础上，提出了基于前端预测识别来分割粘连字符的方法。首先根据粘连字符图像的特征初步确定前端字符的候选字符集，并通过验证候选字符与前端字符图像匹配的必要条件进一步对其筛选，然后使用候选字符的屏蔽码自适应地提取前端字符图像，最后由分类器对提取结果加以验证，达到分割和识别粘连字符的目的。该方法可以适应多种类型的粘连字符，准确性高，且在字符图像质量较差时具有较强的鲁棒性。

AN ALGORITHM FOR SEGMENTATION OF MERGED CHARACTERS BASED ON FOREPART PREDICTION AND RECOGNITION

Segmentation of merged characters is one of difficulties that have attracted a great deal of attention in optical character recognition (OCR). Nowadays, unsuitable segmentation is the primary cause of recognition errors. In this paper, after analyzing several representative related approaches, an algorithm for segmentation and recognition of merged characters based on prediction and recognition is presented. At first, the first, character is predicted and cut from the image with a kind of shielding code, and then examined by the classifier. Finally, all of the characters are recognized one by one. This approach is effective and feasible, and also robust while cutting complicated merging characters even with bad quality of image.