矿井地质记录中手写数字自动识别方法

针对目前矿井地质记录中采用人工判读方式识别手写数字效率较低的问题,提出了一种手写数字自动识别方法,详细介绍了颜色过滤及数字区域定位、数字分割、样本训练、数字识别等步骤。该方法利用计算机图像处理技术实现了对特定区域数字的自动定位和识别,提高了工作效率。     

复杂背景下的粉笔数字字符自动提取方法研究*

根据粉笔字符背景复杂、断笔、残笔、手写字符粘连、重叠等特点,提出了一种复杂背景下粉笔数字字符自动提取方法。通过数字区域定位,获取局部图像并进行二值化;通过数学形态学处理,解决二值化结果中的断笔、残笔等问题;通过改进滴水算法,对粘连字符进行切分,获取单个字符。最后,以板坯号识别为例,开发了板坯号自动识别系统,并成功地应用在板坯的生产过程中,证明了其有效性。     

板坯号自动识别系统的研究与实现

手写数字自动识别应用在热轧炼钢生产流水线中可解放劳动力,提升准确率并提高工作效率。为此根据具体需求,系统地分析了板坯号图像获取与处理、数字识别、板坯号规则匹配等自动化过程,重点研究了板坯定位和分层分类识别等关键技术,实现了板坯的自动定位、手写板坯号的自动识别和系统录入。实验结果与实际应用表明,该系统达到了较高的识别正确率,进而集成并实现了板坯号自动识别系统,具有较好的应用效果。     

基于背景分析的手写数字切分方法

考虑到手写数字串的结构特点,提出一种以分析特定背景区域为主,针对具体粘连数字采用不同分割策略的切分方法。文中引入蓄水池的概念来形象描绘出背景区域中字符间的粘连部分,并从中抽取某些特征对字符的具体粘连形态进行了归纳分类。在分割过程中,根据字符的粘连类型选用不同的滴水算法来求得分割路径。实验结果表明了该方法对于手写数字分割的有效性。     

相连手写数字串的分割和识别

一种新颖的分割相连手写数字串的方法已经问世，它不象传统的分割识别方法，由分割点来专门检测断笔道，以连接上连手写的数字，我们的方法是把潜在分割点作为识别区域之一，并分析笔道轨迹来识别此区域。     

基于卷积神经网络的手写数字识别方法研究

手写数字识别是电子光学字符识别技术的一个分支,如何利用电子计算机自动识别手写在纸张上的阿拉伯数字是较困难的。因此,文章分析了数字识别系统。先对图像进行预处理,再提取数字特征,最后设计卷积神经网络,同时训练和测试所输入的样本。结果表明,全部检测样品的均值识别准确率为71%,具有良好的识别效果。     

可手写计算机识别码的研究

为了使得用手填写的数码能让计算机百分之百地自动识别，本文研究了一种新的可手写计算机识别码，该码是由标有数字的小方格和识别标志组成的二维图形符号，只需简单地在相应的方格上涂黑，即可得到计算机可识别的数码。文中也阐述了以这种码为基础研制计算机数码自动识别系统的方法，及其在财会，商业票据和普查表格数据自动输入中的应用。     

基于模糊模式的手写数字识别

手写体数字识别是一个难度很大,但却具有广阔应用前景的研究课题.文章提出了一种基于模糊模式识别和BP神经网络技术对手写数字进行识别的算法:首先应用BP神经网络技术对手写数字样本进行学习,然后结合模糊模式识别方法进行手写数字识别.实验表明,该方法的正确识别率达95%以上.     

基于字符边缘特征的手写账号切分算法

结合银行票据自动识别系统(Bank-OCR)的开发研究。提出了基于手写数字串边缘特征的手写数字串的切分算法。文章分析了手写数字串的切分难点，提出了数字账号边缘特征的概念，并在利用下边缘特征判断连体字符个数的基础上。利用上下边缘中波峰波谷的特征来对连写数字进行有效切分。实验结果表明对于非限制自由手写数字串的最终切分正切率达到863％，满足了银行票据的预处理要求。     

手写数字识别的原理及应用

随着信息技术的发展,信息建设在我国得到了迅猛的发展,手写数字识别的应用需求越来越广泛.文章从概念、研究背景、研究意义等方面介绍了手写数字识别的原理及实现方法,并介绍了手写数字识别的几个典型应用.     

联机手写维文字符切分算法研究

随着手机短信业务普及,智能手机中实现维吾尔文输入、输出已经是新疆地区1000多万少数民族用户迫切的需求。在连续输入的维吾尔文文章或单词中,切分出一个个的字母,供后续的字母识别使用,字母切分是手写输入识别的核心关键技术。手写维文字符串的分割与字符识别密切相关。采用基于识别的分割方法,系统先通过粗略的图像分析寻找所有可能的切点,在分割的过程中引入识别机制来识别分割碎片,将识别结果经过差值运算后置为每个识别对象的识别可信度,利用移动窗口法找到最佳分割路径。在分类器训练时,采用特征提取来估计分类器参数,得到了性质良好的分类器,试验表明,字符切割准确率高达97.3%。     

A multiple agent architecture for handwritten text recognition

This paper investigates the automatic reading of unconstrained omni-writer handwritten texts. It shows how to endow the reading system with learning faculties necessary to adapt the recognition to each writer's handwriting. In the first part of this paper, we explain how the recognition system can be adapted to a current handwriting by exploiting the graphical context defined by the writer's invariants. This adaptation is guaranteed by activating interaction links over the whole text between the recognition procedures of word entities and those of letter entities. In the second part, we justify the need of an open multiple-agent architecture to support the implementation of such a principle of adaptation. The proposed platform allows to plug expert treatments dedicated to handwriting analysis. We show that this platform helps to implement specific collaboration or cooperation schemes between agents which bring out new trends in the automatic reading of handwritten texts.     

Binary segmentation algorithm for English cursive handwriting recognition

Segmentation in off-line cursive handwriting recognition is a process for extracting individual characters from handwritten words. It is one of the most difficult processes in handwriting recognition because characters are very often connected, slanted and overlapped. Handwritten characters differ in size and shape as well. Hybrid segmentation techniques, especially over-segmentation and validation, are a mainstream to solve the segmentation problem in cursive off-line handwriting recognition. However, the core weakness of the segmentation techniques in the literature is that they impose high risks of chain failure during an ordered validation process. This paper presents a novel Binary Segmentation Algorithm (BSA) that reduces the risks of the chain failure problems during validation and improves the segmentation accuracy. The binary segmentation algorithm is a hybrid segmentation technique and it consists of over-segmentation and validation modules. The main difference between BSA and other techniques in the literature is that BSA adopts an un-ordered segmentation strategy. The proposed algorithm has been evaluated on CEDAR benchmark database and the results of the experiments are very promising.     

Generation and use of handwritten CAPTCHAs

Automated recognition of unconstrained handwriting continues to be a challenging research task. In contrast to the traditional role of handwriting recognition in applications such as postal automation and bank check reading, in this paper, we explore the use of handwriting recognition in designing CAPTCHAs for cyber security. CAPTCHAs (Completely Automatic Public Turing tests to tell Computers and Humans Apart) are automatic reverse Turing tests designed so that virtually all humans can pass the test, but state-of-the-art computer programs will fail. Machine-printed, text-based CAPTCHAs are now commonly used to defend against bot attacks. Our focus is on exploring the generation and use of handwritten CAPTCHAs. We have used a large repository of handwritten word images that current handwriting recognizers cannot read (even when provided with a lexicon) for this purpose and also used synthetic handwritten samples. We take advantage of both our knowledge of the common source of errors in automated handwriting recognition systems as well as the salient aspects of human reading. The simultaneous interplay of several Gestalt laws of perception and the geon theory of pattern recognition (that implies object recognition occurs by components) allows us to explore the parameters that truly separate human and machine abilities.     

Handwritten Chinese text editing and recognition system

This paper describes a handwritten Chinese text editing and recognition system that can edit handwritten text and recognize it with a client-server mode. First, the client end samples and redisplays the handwritten text by using digital ink technics, segments handwritten characters, edits them and saves original handwritten information into a self-defined document. The self-defined document saves coordinates of all sampled points of handwriting characters. Second, the server recognizes handwritten document based on the proposed Gabor feature extraction and affinity propagation clustering (GFAP) method, and returns the recognition results to client end. Moreover, the server can also collect the labeled handwritten characters and fine tune the recognizer automatically. Experimental results on HIT-OR3C database show that our handwriting recognition method improves the recognition performance remarkably.     

Automatic writer identification framework for online handwritten documents using character prototypes

This paper proposes an automatic text-independent writer identification framework that integrates an industrial handwriting recognition system, which is used to perform an automatic segmentation of an online handwritten document at the character level. Subsequently, a fuzzy c-means approach is adopted to estimate statistical distributions of character prototypes on an alphabet basis. These distributions model the unique handwriting styles of the writers. The proposed system attained an accuracy of 99.2% when retrieved from a database of 120 writers. The only limitation is that a minimum length of text needs to be present in the document in order for sufficient accuracy to be achieved. We have found that this minimum length of text is about 160 characters or approximately equivalent to 3 lines of text. In addition, the discriminative power of different alphabets on the accuracy is also reported.     

基于实时样本采集的个性化手写汉字输入系统设计

手写汉字识别是手写汉字输入的基础。目前智能设备中的手写汉字输入法无法根据用户的汉字书写习惯,动态调整识别模型以提升手写汉字的正确识别率。通过对最新深度学习算法及训练模型的研究,提出了一种基于用户手写汉字样本实时采集的个性化手写汉字输入系统的设计方法。该方法将采集用户的手写汉字作为增量样本,通过对服务器端训练生成的手写汉字识别模型的再次训练,使识别模型能够更好地适应该用户的书写习惯,提升手写汉字输入系统的识别率。最后,在该理论方法的基础上,结合新设计的深度残差网络,进行了手写汉字识别的对比实验。实验结果显示,通过引入实时采集样本的再次训练,手写汉字识别模型的识别率有较大幅度的提升,能够更有效的满足用户在智能设备端对手写汉字输入系统的使用需求。     

Handwritten Chinese character recognition using fuzzy image alignment

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. The use of an image-only model means that 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.