Development of a patent document classification and search platform using a back-propagation network

In order to process large numbers of explicit knowledge documents such as patents in an organized manner, automatic document categorization and search are required. In this paper, we develop a document classification and search methodology based on neural network technology that helps companies manage patent documents more effectively. The classification process begins by extracting key phrases from the document set by means of automatic text processing and determining the significance of key phrases according to their frequency in text. In order to maintain a manageable number of independent key phrases, correlation analysis is applied to compute the similarities between key phrases. Phrases with higher correlations are synthesized into a smaller set of phrases. Finally, the back-propagation network model is adopted as a classifier. The target output identifies a patent document’s category based on a hierarchical classification scheme, in this case, the international patent classification (IPC) standard. The methodology is tested using patents related to the design of power hand-tools. Related patents are automatically classified using pre-trained neural network models. In the prototype system, two modules are used for patent document management. The automatic classification module helps the user classify patent documents and the search module helps users find relevant and related patent documents. The result shows an improvement in document classification and identification over previously published methods of patent document management.     

Text segmentation using gabor filters for automatic document processing

There is a considerable interest in designing automatic systems that will scan a given paper document and store it on electronic media for easier storage, manipulation, and access. Most documents contain graphics and images in addition to text. Thus, the document image has to be segmented to identify the text regions, so that OCR techniques may be applied only to those regions. In this paper, we present a simple method for document image segmentation in which text regions in a given document image are automatically identified. The proposed segmentation method for document images is based on a multichannel filtering approach to texture segmentation. The text in the document is considered as a textured region. Nontext contents in the document, such as blank spaces, graphics, and pictures, are considered as regions with different textures. Thus, the problem of segmenting document images into text and nontext regions can be posed as a texture segmentation problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. These filters have been extensively used earlier for a variety of texture segmentation tasks. Here we apply the same filters to the document image segmentation problem. Our segmentation method does not assume any a priori knowledge about the content or font styles of the document, and is shown to work even for skewed images and handwritten text. Results of the proposed segmentation method are presented for several test images which demonstrate the robustness of this technique. This work was supported by the National Science Foundation under NSF grant CDA-88-06599 and by a grant from E. 1. Du Pont De Nemours & Company.     

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。     

东巴象形文字文档图像的文本行自动分割算法研究

以卷积神经网络(CNN)为代表的深度学习技术在图像分类和识别领域表现出了非常优异的性能。但东巴象形文字未有标准、公开的数据集,无法借鉴或使用已有的深度学习算法。为了快速建立权威、有效的东巴文字库,分析已出版东巴文档的版面结构,从文档中提取文本行、东巴字成为了当前的首要任务。因此,结合东巴象形文字文档图像的结构特点,给出了东巴文档图像的文本行自动分割算法。首先利用基于密度和距离的k-均值聚类算法确定了文本行的分类数量和分类标准;然后,通过文字块的二次处理矫正了分割中的错误结果,提高了算法的准确率。在充分利用东巴字文档结构特征的同时,保留了机器学习模型客观、无主观经验影响的优势。通过实验表明,该算法可用于东巴文档图像、脱机手写汉字、东巴经的文本行分割,以及文本行中东巴字和汉字的分割,具有实现简单、准确性高、适应性强的特点,从而为东巴文字库的建立奠定基础。     

Automatic performance evaluation of printed Chinese character recognition systems

Performance evaluation is crucial for improving the performance of OCR systems. However, this is trivial and sophisticated work to do by hand. Therefore, we have developed an automatic performance evaluation system for a printed Chinese character recognition (PCCR) system. Our system is characterized by using real-world data as test data and automatically obtaining the performance of the PCCR system by comparing the correct text and the recognition result of the document image. In addition, our performance evaluation system also provides some evaluation of performance for the segmentation module, the classification module, and the post-processing module of the PCCR system. For this purpose, a segmentation error-tolerant character-string matching algorithm is proposed to obtain the correspondence between the correct text and the recognition result. The experiments show that our performance evaluation system is an accurate and powerful tool for studying deficiencies in the PCCR system. Although our approach is aimed at the PCCR system, the idea also can be applied to other OCR systems.     

基于模式链分析的文本页面图像的分割与分类

为了能对复杂版式的文本图像(如包含镶嵌在文字中的形状不规则的图片区)的页面进行图文分割与分类,提出了一种新的基于模式链分析的文本页面分割与分类算法。该算法首先使用外接矩形框出图像中的所有黑像素,并且存入矩形框链表中,再组合所有相邻的矩形进而形成模式,最后依据各模式的统计特征分类,输出文字区和图片区两类图像。另外,对大图片模式周围个别不确定的模式,本文采用了上下文分类的算法进行再次分类。实验结果表明,该算法不仅运算速度快,而且能够对复杂版式的页面图像进行正确的图文分割和分类。     

文档图象的版面分析-基于数学形态学的方法

提出了基于 Hough变换的结构元素构造方法 (HTSEC)以及基于数学形态学的分割方法 (MMS) ,即首先将灰度文档图象利用半色调技术转换为二值图象 ,利用 Hough变换动态构造结构元素 ,然后利用数学形态学方法对版面进行划分并将其分为文字区、图形区和图象区     

层次化双注意力神经网络模型的情感分析研究

在篇章级的情感分类中由于篇章级文本较长,特征提取较普通句子级分析相对较难,大多方法使用层次化的模型进行篇章文本的情感分析,但目前的层次化模型多以循环神经网络和注意力机制为主,单一的循环神经网络结构提取的特征不够明显。本文针对篇章级的情感分类任务,提出一种层次化双注意力神经网络模型。首先对卷积神经网络进行改进,构建词注意力卷积神经网络。然后模型从两个层次依次提取篇章特征,第一层次使注意力卷积神经网络发现每个句子中的重要词汇,提取句子的词特征,构建句子特征向量;第二层次以循环神经网络获取整个篇章的语义表示,全局注意力机制发现篇章中每个句子的重要性,分配以不同的权重,最后构建篇章的整体语义表示。在IMDB、YELP 2013、YELP 2014数据集上的实验表明,模型较当前最好的模型更具优越性。     

Segmentation and classification of mixed text/graphics/image documents

In this paper, a feature-based document analysis system is presented which utilizes domain knowledge to segment and classify mixed text/graphics/image documents. In our approach, we first perform a run-length smearing operation followed by the stripe merging procedure to segment the blocks embedded in a document. The classification task is then performed based on the domain knowledge induced from the primitives associated with each type of medium. Proper use of domain knowledge is proved to be effective in accelerating the segmentation speed and decreasing the classification error. The experimental study reveals the feasibility of the new technique in segmenting and classifying mixed text/graphics/image documents.     

一种面向中文敏感网页识别的文本分类方法

提出了一种面向中文敏感网页识别的文本分类方法,主要包括中文分词、停用词表的建立、特征选择、分类器等4个部分.为丰富中文分词词库,提出了一种以词频统计为主、以人工判决为辅并标注词性的新词识别算法;提出了一种停用词表的建立算法,据此建立了含300个停用词的停用词表;采用开方拟合检验统计量方法作为特征选择方法,并确定了400...     

串频统计和词形匹配相结合的汉语自动分词系统

本文介绍了一种汉语自动分词软件系统,该系统对原文进行三遍扫描:第一遍,利用切分标记将文本切分成汉字短串的序列;第二遍,根据各短串的每个子串在上下文中的频度计算其权值,权值大的子串视为候选词;第三遍,利用候选词集和一部常用词词典对汉字短串进行切分。实验表明,该分词系统的分词精度在1.5%左右,能够识别大部分生词,特别适用于文献检索等领域。     

Color clustering and learning for image segmentation based on neural networks

An image segmentation system is proposed for the segmentation of color image based on neural networks. In order to measure the color difference properly, image colors are represented in a modified L/sup */u/sup */v/sup */ color space. The segmentation system comprises unsupervised segmentation and supervised segmentation. The unsupervised segmentation is achieved by a two-level approach, i.e., color reduction and color clustering. In color reduction, image colors are projected into a small set of prototypes using self-organizing map (SOM) learning. In color clustering, simulated annealing (SA) seeks the optimal clusters from SOM prototypes. This two-level approach takes the advantages of SOM and SA, which can achieve the near-optimal segmentation with a low computational cost. The supervised segmentation involves color learning and pixel classification. In color learning, color prototype is defined to represent a spherical region in color space. A procedure of hierarchical prototype learning (HPL) is used to generate the different sizes of color prototypes from the sample of object colors. These color prototypes provide a good estimate for object colors. The image pixels are classified by the matching of color prototypes. The experimental results show that the system has the desired ability for the segmentation of color image in a variety of vision tasks.     

一种基于联合深度神经网络的食品安全信息情感分类模型

针对因中文食品安全文本特征表达困难,而造成语义信息缺失进而导致分类器准确率低下的问题,提出一种基于深度神经网络的跨文本粒度情感分类模型。以食品安全新闻报道为目标语料,采用无监督的浅层神经网络初始化文本的词语级词向量。引入递归神经网络,将预训练好的词向量作为下层递归神经网络(Recursive Neural Network)的输入层,计算得到具备词语间语义关联性的句子特征向量及句子级的情感倾向输出,同时动态反馈调节词向量特征,使其更加接近食品安全特定领域内真实的语义表达。然后,将递归神经网络输出的句子向量以时序逻辑作为上层循环神经网络(Recurrent Neural Network)的输入,进一步捕获句子结构的上下文语义关联信息,实现篇章级的情感倾向性分析任务。实验结果表明,联合深度模型在食品安全新闻报道的情感分类任务中具有良好的效果,其分类准确率和F1值分别达到了86.7%和85.9%,较基于词袋思想的SVM模型有显著的提升。     

Hierarchical document classification using automatically generated hierarchy

Automated text categorization has witnessed a booming interest with the exponential growth of information and the ever-increasing needs for organizations. The underlying hierarchical structure identifies the relationships of dependence between different categories and provides valuable sources of information for categorization. Although considerable research has been conducted in the field of hierarchical document categorization, little has been done on automatic generation of topic hierarchies. In this paper, we propose the method of using linear discriminant projection to generate more meaningful intermediate levels of hierarchies in large flat sets of classes. The linear discriminant projection approach first transforms all documents onto a low-dimensional space and then clusters the categories into hier- archies accordingly. The paper also investigates the effect of using generated hierarchical structure for text classification. Our experiments show that generated hierarchies improve classification performance in most cases.     

Densemask RCNN: A Hybrid Model for Skin Burn Image Classification and Severity Grading

In the medical image processing, automatic segmentation of burn images is one of the critical tasks in the classification of skin burn into normal and burn area. Traditional models identify the burns from the image and distinguish the region as burn and non-burn regions. However, the earlier models cannot accurately classify the wound region and also requires more time in the prediction of burns. Also, the burn depth analysis is an important factor for the calculation of the percentage of burn depth i.e. degree of severity is analyzed by Total body surface area (TBSA). For those issues, we design a hybrid approach named DenseMask Regional convolutional neural network (RCNN) approach for segmenting the skin burn region based on the various degrees of burn severity. In this, hybrid integration of Mask-region based convolution neural network CNN (Mask R-CNN) and dense pose estimation are integrated into DenseMask RCNN that calculate the full-body human pose and performs semantic segmentation. At first, we use the Residual Network with a dilated convolution using a weighted mapping model to generate the dense feature map. Then the feature map is fed into the Region proposal network (RPN) which utilizes a Feature pyramid network (FPN) to detect the objects at different scales of location and pyramid level from the input images. For the accurate alignment of pixel-to-pixel labels, we introduce a Region of interest (RoI)-pose align module that properly aligns the objects based on the human pose with the characteristics of scale, right-left, translation, and left–right flip to a standard scale. After the alignment task, a cascaded fully convolutional architecture is employed on the top of the RoI module that performs mask segmentation and dense pose regression task simultaneously. Finally, the transfer learning model classifies the detected burn regions into three classes of wound depths. Experimental analysis is performed on the burn dataset and the result obtained shows better accuracy than the state-of-art approaches.

     

改进的概率神经网络的语义区域分类

针对彩色图像信息量大,分割效果自适应性差的问题,对图像语义区域的分割精度进行控制,提取图像的纹理特征值,再通过改进后的概率神经网络模型对测试样本做分类测试,达到提高图像语义提取和分类准确性的目的。实验表明,改进后的概率神经网络对彩色图像语义区域分类的正确性由原先的70%提高到90%,具有较好的分类效果。     

Bottom-up document segmentation method based on textural features

A bottom-up approach to segmentation of a scanned document into background, text, and image regions is considered. The image is partitioned into blocks at the first step. A series of texture features is computed for each block. The block type is determined on the basis of these features. Different variants of block arrangement and size, 26 texture variables, and four block type classification algorithms have been considered. The block type is corrected on the basis of adjacent region analysis at the second step. The error matrix and ICDAR 2007 criterion are used for result estimation.     

Consensus-based clustering for document image segmentation

Segmentation of a document image plays an important role in automatic document processing. In this paper, we propose a consensus-based clustering approach for document image segmentation. In this method, the foreground regions of a document image are grouped into a set of primitive blocks, and a set of features is extracted from them. Similarities among the blocks are computed on each feature using a hypothesis test-based similarity measure. Based on the consensus of these similarities, clustering is performed on the primitive blocks. This clustering approach is used iteratively with a classifier to label each primitive block. Experimental results show the effectiveness of the proposed method. It is further shown in the experimental results that the dependency of classification performance on the training data is significantly reduced.     

基于WordNet概念向量空间模型的文本分类

文章提出了一种文本特征提取方法,以WordNet语言本体库为基础,以同义词集合概念代替词条,同时考虑同义词集合间的上下位关系,建立文本的概念向量空间模型作为文本特征向量,使得在训练过程中能够提取出代表类别的高层次信息。实验结果表明,当训练文本集合很小时,方法能够较大地提高文本的分类准确率。