Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition

Semi-Markov conditional random fields (semi-CRFs) are usually trained with maximum a posteriori (MAP) criterion which adopts the 0/1 cost for measuring the loss of misclassification. In this paper, based on our previous work on handwritten Chinese/Japanese text recognition (HCTR) using semi-CRFs, we propose an alternative parameter learning method by minimizing the risk on the training set, which has unequal misclassification costs depending on the hypothesis and the ground-truth. Based on this framework, three non-uniform cost functions are compared with the conventional 0/1 cost, and training data selection is incorporated to reduce the computational complexity. In experiments of online handwriting recognition on databases CASIA-OLHWDB and TUAT Kondate, we compared the performances of the proposed method with several widely used learning criteria, including conditional log-likelihood (CLL), softmax-margin (SMM), minimum classification error (MCE), large-margin MCE (LM-MCE) and max-margin (MM). On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.     

Supporting electronic ink databases

The emergence of the pen as the main interface device for personal digital assistants and pen-computers has made handwritten text, and more generally ink, a first-class object. As for any other type of data, the need of retrieval is a prevailing one. Retrieval of handwritten text is more difficult than that of conventional data since it is necessary to identify a handwritten word given slightly different variations in its shape. The current way of addressing this is by using handwriting recognition, which is prone to errors and limits the expressiveness of ink. Alternatively, one can retrieve from the database handwritten words that are similar to a query handwritten word using techniques borrowed from pattern and speech recognition. In this paper, an indexing technique based on Hidden Markov Models is proposed. Its implementation and its performance is reported in this paper.     

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.     

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.     

Unconstrained handwritten document retrieval

With the ever-increasing growth of the World Wide Web, there is an urgent need for an efficient information retrieval system that can search and retrieve handwritten documents when presented with user queries. However, unconstrained handwriting recognition remains a challenging task with inadequate performance thus proving to be a major hurdle in providing robust search experience in handwritten documents. In this paper, we describe our recent research with focus on information retrieval from noisy text derived from imperfect handwriting recognizers. First, we describe a novel term frequency estimation technique incorporating the word segmentation information inside the retrieval framework to improve the overall system performance. Second, we outline a taxonomy of different techniques used for addressing the noisy text retrieval task. The first method uses a novel bootstrapping mechanism to refine the OCR’ed text and uses the cleaned text for retrieval. The second method uses the uncorrected or raw OCR’ed text but modifies the standard vector space model for handling noisy text issues. The third method employs robust image features to index the documents instead of using noisy OCR’ed text. We describe these techniques in detail and also discuss their performance measures using standard IR evaluation metrics.     

维吾尔语笔迹鉴别方法研究

笔迹鉴别是通过机器分析手写笔迹风格的差异特征来判断书写人身份的一门科学与技术。就像语音、指纹、虹膜和脸谱等生物特征识别技术一样是一个典型的模式识别问题。笔迹鉴别可分为在线、离线两种。笔迹鉴别方法可以分为两大类：文本依存的方法和文本独立的方法。主要针对离线维吾尔语手写体笔迹鉴别方法展开研究,力求提取笔迹图像的全局特征,以提供更多更有效的鉴别信息,结合维吾尔语自身特点对与文本无关的笔迹鉴别中预处理和特征提取技术进行了细致的研究。     

Synthetic handwritten CAPTCHAs

CAPTCHAs (completely automated public Turing test to tell computers and humans apart) are in common use today as a method for performing automated human verification online. The most popular type of CAPTCHA is the text recognition variety. However, many of the existing printed text CAPTCHAs have been broken by web-bots and are hence vulnerable to attack. We present an approach to use human-like handwriting for designing CAPTCHAs. A synthetic handwriting generation method is presented, where the generated textlines need to be as close as possible to human handwriting without being writer-specific. Such handwritten CAPTCHAs exploit the differential in handwriting reading proficiency between humans and machines. Test results show that when the generated textlines are further obfuscated with a set of deformations, machine recognition rates decrease considerably, compared to prior work, while human recognition rates remain the same.