Hybrid contextural text recognition with string matching

Sinha, R.M.K.; Prasada, B.; Houle, G.; Sabourin, Michael

doi:10.1109/34.232077

Cited by 43 publications

(12 citation statements)

References 17 publications

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“…To remove incorrect segmentation points obtain as a trade-off during dissection, a set of hypothesis are tested by merging segments of the image and invoking a classifier to score the combination of segments. The literature is replete with hybrid approaches proposed by a number of researchers to optimize algorithms with linear searching techniques, contextual and lexicon knowledge (Casey 1992;Kimura et al 1992;Favata and Srihari 1992;Bruel 1994;Sinha et al 1993;Kim and Govindaraju 1997;Kim et al 2000;Hanhong 2002;Grandidier 2003;Koch et al 2004;Farah et al 2005 etc) More recently Rehman and Dzulkifli (2008) proposed a new fast segmentation approach for off-line cursive handwritten words with accuracy up to 91.21 on a subset of IAM database. Authors proposed certain rules to analyze ligatures along with knowledge of character shape.…”

Section: Hybrid Strategiesmentioning

confidence: 99%

Off-line cursive script recognition: current advances, comparisons and remaining problems

Rehman

Saba

2011

Artif Intell Rev

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This paper presents detailed review in the field of off-line cursive script recognition. Various methods are analyzed that have been proposed to realize the core of script recognition in a word recognition system. These methods are discussed in view of the two most important properties of such systems: size and nature of the lexicon involved and whether or not a segmentation stage is present. Script recognition techniques are classified into three categories: firstly, segmentation-free methods or holistic approaches, that compare a sequence of observations derived from whole word image with similar references of words in the small lexicon. Secondly, segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word similar to human-like reading technique, in which secure features found all over the word are used to boot-strap a few candidates for a final evaluation phase; thirdly, hybrid approaches. Additionally, different feature extraction techniques are elaborated in conjunction with the classification process. In this scenario, implications of single and multiple classifiers are also observed. Finally, remaining problems are highlighted along with possible suggestion and strategies to solve them.

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Section: Hybrid Strategiesmentioning

confidence: 99%

Off-line cursive script recognition: current advances, comparisons and remaining problems

Rehman

Saba

2011

Artif Intell Rev

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“…The random variables whose joint probabilities must be estimated are letter, word, or part-of-speech labels. Linguistic context is always order-dependent, and therefore often modeled with transition frequencies in Markov Chains, Hidden Markov Models, and Markov Random Fields [16,17,18,19,20,21,22,23,24,25,26,27,28]. Linguistic variables are usually assumed to be independent of character shape, even though titles and headings in large or bold type have a different language structure than plain text.…”

Section: Language Modelsmentioning

confidence: 99%

Adaptive and Interactive Approaches to Document Analysis

Nagy¹,

Veeramachaneni

2008

Machine Learning in Document Analysis and Recognition

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Summary. This chapter explores three aspects of learning in document analysis: (1) field classification, (2) interactive recognition, and (3) portable and networked applications. Context in document classification conventionally refers to language context, i.e., deterministic or statistical constraints on the sequence of letters in syllables or words, and on the sequence of words in phrases or sentences. We show how to exploit other types of statistical dependence, specifically the dependence between the shape features of several patterns due to the common source of the patterns within a field or a document. This type of dependence leads to field classification, where the features of some patterns may reveal useful information about the features of other patterns from the same source but not necessarily from the same class. We explore the relationship between field classification and the older concepts of unsupervised learning and adaptation. Human interaction is often more effective interspersed with algorithmic processes than only before or after the automated parts of the process. We develop a taxonomy for interaction during training and testing, and show how either human-initiated and machine-initiated interaction can lead to human and machine learning. In a section on new technologies, we discuss how new cameras and displays, web-wide access, interoperability, and essentially unlimited storage provide fertile new approaches to document analysis.

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“…Contextual information helps improve classification accuracy. Many OCR systems use it, and its effectiveness has been demonstrated in previous work [66,67]. The key is to model the statistical dependency among neighboring components.…”

Section: Summary and Future Workmentioning

confidence: 99%

Handwriting Identification, Matching, and Indexing in Noisy Document Images

Zheng¹

2006

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Throughout history, handwriting has been the primary means of recording information that is persevered across both time and space. With the coming of the electronic document era, we are challenged with making an enormous amount of handwritten documents available for electronic access. Though many handwritten documents contain only handwriting, now, more are mixed with printed text, noise, and background patterns. The mixture of handwriting with other components presents a great challenge for making an original document electronically accessible.Many handwritten documents come together with a special background pattern, rule lines, which are printed on the paper to guide writing. After digitization, rule lines will touch text and cause problems for further document image analysis if they are not detected and removed. In this dissertation, we present a rule line detection algorithm based on hidden Markov model (HMM) decoding, achieving both high detection accuracy and a low false alarm rate. After detection, line removal is performed by line width thresholding.Handwriting often mixes with printed text, such as signatures and annotations on a business letter. Handwriting in a printed document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content. The data set we are processing is noisy, which makes the problem more challenging. In this dissertation, we first segment the document at a suitable level, and then classify each segmented block as machine printed text, handwriting, or noise. Markov random field (MRF) based post-processing is exploited to refine the classification results.The identified handwriting may be further analyzed. In this dissertation, we propose a novel point-pattern based handwriting matching technique and apply it for handwriting synthesis and retrieval. We formulate point matching as an optimization problem trying to preserve the local neighborhood structures. After establishing the correspondence between two handwriting samples, we warp one sample toward the other using the thin plate spline (TPS) deformation model to synthesize new handwriting samples. We also apply our matching algorithm for handwriting retrieval since it is much easier to define robust features based on the matching

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Hybrid contextural text recognition with string matching

Cited by 43 publications

References 17 publications

Off-line cursive script recognition: current advances, comparisons and remaining problems

Off-line cursive script recognition: current advances, comparisons and remaining problems

Adaptive and Interactive Approaches to Document Analysis

Handwriting Identification, Matching, and Indexing in Noisy Document Images

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