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。     

A categorization system for handwritten documents

This paper presents a complete system able to categorize handwritten documents, i.e. to classify documents according to their topic. The categorization approach is based on the detection of some discriminative keywords prior to the use of the well-known tf-idf representation for document categorization. Two keyword extraction strategies are explored. The first one proceeds to the recognition of the whole document. However, the performance of this strategy strongly decreases when the lexicon size increases. The second strategy only extracts the discriminative keywords in the handwritten documents. This information extraction strategy relies on the integration of a rejection model (or anti-lexicon model) in the recognition system. Experiments have been carried out on an unconstrained handwritten document database coming from an industrial application concerning the processing of incoming mails. Results show that the discriminative keyword extraction system leads to better recall/precision tradeoffs than the full recognition strategy. The keyword extraction strategy also outperforms the full recognition strategy for the categorization task.     

用Visual C++构造用于手写汉字识别的模拟系统

手写汉字识别是模式识别领域极具应用前景的研究课题之一。本文介绍了用Visual C^ 6.0构造用于研究手写汉字识别的模拟系统，用软件方式实现手写输入。该系统使用方便，使用者可以将精力集中在手写汉字特征提取、识别速度和识别率等方面。     

Using topic models for OCR correction

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers perform adequately on constrained handwritten documents which typically use a restricted vocabulary (lexicon). But in the case of unconstrained handwritten documents, state-of-the-art word recognition accuracy is still below the acceptable limits. The objective of this research is to improve word recognition accuracy on unconstrained handwritten documents by applying a post-processing or OCR correction technique to the word recognition output. In this paper, we present two different methods for this purpose. First, we describe a lexicon reduction-based method by topic categorization of handwritten documents which is used to generate smaller topic-specific lexicons for improving the recognition accuracy. Second, we describe a method which uses topic-specific language models and a maximum-entropy based topic categorization model to refine the recognition output. We present the relative merits of each of these methods and report results on the publicly available IAM database.     

Machine Learning for Intelligent Processing of Printed Documents

A paper document processing system is an information system component which transforms information on printed or handwritten documents into a computer-revisable form. In intelligent systems for paper document processing this information capture process is based on knowledge of the specific layout and logical structures of the documents. This article proposes the application of machine learning techniques to acquire the specific knowledge required by an intelligent document processing system, named WISDOM++, that manages printed documents, such as letters and journals. Knowledge is represented by means of decision trees and first-order rules automatically generated from a set of training documents. In particular, an incremental decision tree learning system is applied for the acquisition of decision trees used for the classification of segmented blocks, while a first-order learning system is applied for the induction of rules used for the layout-based classification and understanding of documents. Issues concerning the incremental induction of decision trees and the handling of both numeric and symbolic data in first-order rule learning are discussed, and the validity of the proposed solutions is empirically evaluated by processing a set of real printed documents.     

面向机器学习的流式文档逻辑结构标注方法研究

针对采用机器学习方法识别流式文档结构时语料库稀少、语料标注复杂的问题,该文在研究文档的逻辑结构和编辑语义特征的基础上,确立流式文档逻辑结构标注体系,并提出一种三段式的半自动文档逻辑结构标注方法: 第一阶段通过机助人工实现文档元数据的分离式标注,第二阶段自动重建逻辑结构,第三阶段自动填充特征向量。实验结果表明,该文提出的文档逻辑结构标注方法能够节省人工成本、提高机器学习算法对文档结构识别的准确率与召回率,F值达到97.5%。     

A Nom historical document recognition system for digital archiving

A Nom historical document recognition system is being developed for digital archiving that uses image binarization, character segmentation, and character recognition. It incorporates two versions of off-line character recognition: one for automatic recognition of scanned and segmented character patterns (7660 categories) and the other for user handwritten input (32,695 categories). This separation is used since including less frequently appearing categories in automatic recognition increases the misrecognition rate without reliable statistics on the Nom language. Moreover, a user must be able to check the results and identify the correct categories from an extended set of categories, and a user can input characters by hand. Both versions use the same recognition method, but they are trained using different sets of training patterns. Recursive X–Y cut and Voronoi diagrams are used for segmentation; k–d tree and generalized learning vector quantization are used for coarse classification; and the modified quadratic discriminant function is used for fine classification. The system provides an interface through which a user can check the results, change binarization methods, rectify segmentation, and input correct character categories by hand. Evaluation done using a limited number of Nom historical documents after providing ground truths for them showed that the two stages of recognition along with user checking and correction improved the recognition results significantly.     

计算机文档的用户界面及其设计方法

计算机文档是软件系统的重要组成部分，它帮助用户与软件系统进行沟通，是软件用户界面的重要补充，本文针对目前计算机文档特别是用户文档不如人意的开发状况，强调用户文档对于软件可用性的重要性，提出用户文档生命周期的概念指导计算机文档开发，并对文档结构设计和文档用户界面设计进行了具体的设计。     

基于决策树的快速在线手写数字识别技术

本文提出了一种快速的在线手写数字识别方法，该法采用书写笔划走势对手写数字进行建模，运用决策树学习算法进行数字分类识别。数字笔划走势特征提取简单、区分度高、对用户不敏感，实现了有限的资源条件下的高速识别，同时保证了方法的良好用户适应性；决策树学习算法分类情况全面，保证了方法的高识别率。实验结果表明：该方法既具有简单高效的特点，又具备很好的用户适应性。     

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.     

Handwritten Chinese text line segmentation by clustering with distance metric learning

Separating text lines in unconstrained handwritten documents remains a challenge because the handwritten text lines are often un-uniformly skewed and curved, and the space between lines is not obvious. In this paper, we propose a novel text line segmentation algorithm based on minimal spanning tree (MST) clustering with distance metric learning. Given a distance metric, the connected components (CCs) of document image are grouped into a tree structure, from which text lines are extracted by dynamically cutting the edges using a new hypervolume reduction criterion and a straightness measure. By learning the distance metric in supervised learning on a dataset of pairs of CCs, the proposed algorithm is made robust to handle various documents with multi-skewed and curved text lines. In experiments on a database with 803 unconstrained handwritten Chinese document images containing a total of 8,169 lines, the proposed algorithm achieved a correct rate 98.02% of line detection, and compared favorably to other competitive algorithms.     

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.     

融合LeNet-5改进的扫描文档手写日期识别

研究LeNet-5在扫描文档中手写体日期字符识别的应用,由于文档扫描的过程中会引入各种噪声,特别是光照和颜色干扰,直接使用LeNet-5算法不能取得较好效果。先在整份文档中对特定待识别字符的进行定位和划分,并对划分出的字符图像进行去噪、灰度化和二值化处理等预处理,接着将字符图像分割成一个个单个字符,然后在LeNet-5网络基础上结合模型匹配法实现对手写体日期字符的识别。分析在不同参数组合下的识别效果,调整算法模型参数有效地提升了模型对于实际对象的性能,实现出一种能够对手写体日期字符集实现较好识别效果的算法。实验结果表明了算法的有效性,并应用于具体工程实践。     

A graph-based approach for segmenting touching lines in historical handwritten documents

Text line segmentation in handwritten documents is an important task in the recognition of historical documents. Handwritten document images contain text lines with multiple orientations, touching and overlapping characters between consecutive text lines and different document structures, making line segmentation a difficult task. In this paper, we present a new approach for handwritten text line segmentation solving the problems of touching components, curvilinear text lines and horizontally overlapping components. The proposed algorithm formulates line segmentation as finding the central path in the area between two consecutive lines. This is solved as a graph traversal problem. A graph is constructed using the skeleton of the image. Then, a path-finding algorithm is used to find the optimum path between text lines. The proposed algorithm has been evaluated on a comprehensive dataset consisting of five databases: ICDAR2009, ICDAR2013, UMD, the George Washington and the Barcelona Marriages Database. The proposed method outperforms the state-of-the-art considering the different types and difficulties of the benchmarking data.