笔迹图像中的单个汉字字符分割

提出了一套完整的针对单字的笔迹图像分割算法,选用不同的笔迹样本作了验证实验,对实现单字分割做了全面的阐述论证。将模板分割算法中的行分割、字分割、单字图像库建立和基于模板匹配的分割算法结合在一起,提高了算法的运算速度和精确度。利用50幅笔迹样本进行测试,92%的单字分割样本可以作为单字模板,应用模板匹配分割算法92%的样本可以实现单字提取。     

Straight line approximation and 1D representation of off-line handwritten text

Algorithms to process off-line Arabic handwriting before recognition are presented. First, an algorithm that converts smoothed and thinned images into straight line approximations is described. Second, an algorithm is developed to obtain a 1D representation of off-line Arabic handwriting. This is achieved by first finding the start-end pair of vertices of writing. Then a stroke is traversed from the start to the end vertex by solving the Chinese postman's problem for its graph. Special rules are applied to enforce temporal information on the stroke to obtain the most likely traversal that is consistent with Arabic handwriting. Finally, an algorithm is suggested to reduce straight line approximations to other approximations in which loops are represented by vertices with features. In testing, 2256 unconstrained handwritten strokes, written by six writes, were used. In 96.5% of the samples, the algorithm restored the actual temporal information.     

局部关联度最优的手写汉字骨架提取

目的 研究手写汉字图像时,骨架是最为常见的切入点之一。利用传统细化算法提取手写汉字骨架,容易在笔画交叉等情况复杂的区域产生形变。针对此问题,提出一种基于局部关联度的手写汉字骨架提取算法。方法 首先对手写汉字图像进行细化以获取原始骨架,按照端点、普通点和复杂点3种类别标注骨架点;利用8邻域窗口扫描相互连通的复杂点,检测并提取复杂区域;删除复杂区域,将原始骨架拆分为若干简单笔画段,形变部分在此过程中被一并移除;提取局部子段,根据笔画段间的方向差异程度和曲率变化程度,计算局部关联度;制定一种局部关联度最优的连接策略,对满足连接条件的笔画段进行插值补偿,从而修正形变,并得到完整的汉字骨架。结果 对于600个实验样本,从骨架直接检测复杂区域所得结果十分接近理想情况,而轮廓法所得数量是理论值的2.5倍;基于局部关联度重组笔画段,绝大多数形变得到修正,重组后的骨架符合真实拓扑结构;以标准骨架为参考,骨架提取准确率达到了98.41%。结论 局部关联度最优的手写汉字骨架提取算法,能够有效检测复杂区域,对形变具有良好的修正作用,提取所得骨架能够正确反映复杂笔画间的位置结构关系,是一种实用有效的骨架提取方法。     

Text line and word segmentation of handwritten documents

In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents.     

Text line extraction from multi-skewed handwritten documents

A novel text line extraction technique is presented for multi-skewed document images of handwritten English or Bengali text. It assumes that hypothetical water flows, from both left and right sides of the image frame, face obstruction from characters of text lines. The stripes of areas left unwetted on the image frame are finally labelled for extraction of text lines. The success rate of the technique, as observed experimentally, are 90.34% and 91.44% for handwritten Bengali and English document images, respectively. The work may contribute significantly for the development of applications related to optical character recognition of Bengali/English text.     

灰阶边缘的细化

本文提出了一种改进了的灰阶图象Sobel边缘细化新方法,通过对灰阶边缘图多次Sobel边缘细化结果的叠加使边缘点的数值拔高、变陡,再通过灰阶边缘图中各3x3邻域内取数值最大的三个点作边缘,得到单点宽的边缘-其中后者也适用于其它边缘检测算子.此方法处理简单、效果明显,为从图象中提取模糊边缘和微弱边缘、提高定位精度,提供了一种有效的手段.     

汉藏机器翻译的特点与手写汉字切分分析研究

汉藏机器翻译的研究是一项有益而复杂的课题。本文的工作主要有两项, 一是分析了汉藏机器翻译中藏语句子特点,给出了一些汉藏翻译句型的举例;二是阐述了在汉藏机器翻译研究中一项前期的辅助工作,即设计了手写汉字切分方法和算法。     

特征离散点计算在手写文本行分割中的应用

将图像分析实践中的经验知识与粒计算的基本思想相结合,总结形成了特征离散点计算,并将其应用于自然手写汉字文本行分割当中。在特征离散点计算的结构化问题求解框架下,提出了一种反馈式分列行投影文本行分割方法,分为特征离散点选择、特征离散点采样与优化、特征离散点编组与反馈以及行边缘优化四个环节。该方法在哈尔滨工业大学多人手写数据库上取得了相对以往算法较好的实验结果,同时分割速度较快。     

FPTA快速细化算法在脱机手写体汉字识别中的应用

阐述了将FPTA快速细化算法应用于脱机手写体汉字识别中,该算法提出了一种11象素支撑窗口,能有效地抑制样本中的“毛刺“,快速提取保持原始汉字样本连通性和拓扑性的骨架,它的处理速度和细化质量明显优于现有的一些细化算法,根据实验结果,该算法是一种有效的汉字样本细化算法。     

回归—聚类联合框架下的手写文本行提取

目的 手写文本行提取是文档图像处理中的重要基础步骤,对于无约束手写文本图像,文本行都会有不同程度的倾斜、弯曲、交叉、粘连等问题。利用传统的几何分割或聚类的方法往往无法保证文本行边缘的精确分割。针对这些问题提出一种基于文本行回归-聚类联合框架的手写文本行提取方法。方法 首先,采用各向异性高斯滤波器组对图像进行多尺度、多方向分析,利用拖尾效应检测脊形结构提取文本行主体区域,并对其骨架化得到文本行回归模型。然后,以连通域为基本图像单元建立超像素表示,为实现超像素的聚类,建立了像素-超像素-文本行关联层级随机场模型,利用能量函数优化的方法实现超像素的聚类与所属文本行标注。在此基础上,检测出所有的行间粘连字符块,采用基于回归线的k-means聚类算法由回归模型引导粘连字符像素聚类,实现粘连字符分割与所属文本行标注。最后,利用文本行标签开关实现了文本行像素的操控显示与定向提取,而不再需要几何分割。结果 在HIT-MW脱机手写中文文档数据集上进行文本行提取测试,检测率DR为99.83%,识别准确率RA为99.92%。结论 实验表明,提出的文本行回归-聚类联合分析框架相比于传统的分段投影分析、最小生成树聚类、Seam Carving等方法提高了文本行边缘的可控性与分割精度。在高效手写文本行提取的同时,最大程度地避免了相邻文本行的干扰,具有较高的准确率和鲁棒性。     

工程图纸输入与自动识别的改进细化算法

系统地介绍了两种典型的细化算法OPTA算法和Hilditch算法的原理和计算机实现，分析了OPTA算法和Hilditch算法的性能与不足，并提出了一种改进的Hilditch细化算法。该算法通过借鉴OPTA算法中的模板方法有效地减小了图像骨架偏移和毛刺的影响，经实验结果观察，改进的算法使图像细化结果得到较大改善。     

大幅图像的分块细化加速算法

文章在分析大规模二值图像特点和细化算法基本原理的基础上,提出一种分块细化大幅图像的算法。该算法首先将大幅图像分割成若干子块,然后分别对各子块进行细化,最后将子块细化的结果拼接成原图的骨架。算法有效地解决了大幅图像细化处理的时空矛盾,提高了大幅图像的细化速度。     

一种字符图像线段提取及细化算法

为了对字符图像进行稳定有效的细化和线段特征提取，提出了一种同时完成字符图像线段提取和细化的算法。在定义字符图像的线段区域、交点区域和子段区域，同时指出待细化字符图像是线段区域的集合的基础上，该算法首先基于图像行(列)连通像素段来提取字符图像中所有的交点区域和子段区域，然后对属于同一线段区域的所有子段区域和交点区域进行合并，最后用Bernstein—Bezier多项式方法拟合每个线段区域的骨架。该算法的鲁棒性分析及实验结果表明，该算法可取得稳定有效的细化及线段提取效果。     

Combining diverse on-line and off-line systems for handwritten text line recognition

In this paper we present a multiple classifier system (MCS) for on-line handwriting recognition. The MCS combines several individual recognition systems based on hidden Markov models (HMMs) and bidirectional long short-term memory networks (BLSTM). Beside using two different recognition architectures (HMM and BLSTM), we use various feature sets based on on-line and off-line features to obtain diverse recognizers. Furthermore, we generate a number of different neural network recognizers by changing the initialization parameters. To combine the word sequences output by the recognizers, we incrementally align these sequences using the recognizer output voting error reduction framework (ROVER). For deriving the final decision, different voting strategies are applied. The best combination ensemble has a recognition rate of 84.13%, which is significantly higher than the 83.64% achieved if only one recognition architecture (HMM or BLSTM) is used for the combination, and even remarkably higher than the 81.26% achieved by the best individual classifier. To demonstrate the high performance of the classification system, the results are compared with two widely used commercial recognizers from Microsoft and Vision Objects.     

一种用于脱机手写体女书字符切分的方法*

针对目前国内文字切分主要应用于汉字、英文等,而对于濒危少数民族文字缺乏较为实用的切分工具的问题,提出了一种用于脱机手写体女书字符的切分方法。该方法结合投影法和连通域进行粗切分,对交叉字符采用曲线切分,对粘连字符采用改进的滴水算法切分,经实验证明该方法能够取得较好的切分效果。该方法为女书字符切分提供了新思路。     

Learning-based word spotting system for Arabic handwritten documents

The retrieval of information from scanned handwritten documents is becoming vital with the rapid increase of digitized documents, and word spotting systems have been developed to search for words within documents. These systems can be either template matching algorithms or learning based. This paper presents a coherent learning based Arabic handwritten word spotting system which can adapt to the nature of Arabic handwriting, which can have no clear boundaries between words. Consequently, the system recognizes Pieces of Arabic Words (PAWs), then re-constructs and spots words using language models. The proposed system produced promising result for Arabic handwritten word spotting when tested on the CENPARMI Arabic documents database.     

汉字手写式键盘输入方法的设计与实现

设计了一种汉字手写式键盘输入法,给出了基于快速模糊匹配方法的笔画智能生成算法及其实现。该方法在键盘上将汉字笔画模拟表示为笔画轨迹的起点、拐点、中间点和落点的键序列,通过构造笔画的网状搜索空间,实现了键序列到笔画序列集合的转换,进而实现汉字输入。本方法无需记忆键位,会写汉字就会输入。     

基于连通域特征的维吾尔手写文本行分割

针对维吾尔文手写体文本中行分割问题,基于连通域大小将图像中文字分为三类,提出了自适应涂抹细化算法,对主体文本行进行定位;并对第三类连通域中相邻两文本行间粘连的字符进行切割;此外,利用重心范围内的邻域搜索算法,解决了剩余笔画的文本行归附问题。实验结果表明,该方法与常见的水平投影法,分段投影法,及涂抹方法相比具有更好的分割效果。