Recognition of cursive Roman handwriting: past, present and future

Bunke, Horst

doi:10.1109/icdar.2003.1227707

Cited by 98 publications

(56 citation statements)

References 111 publications

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“…postal address identification [3], Bank check processing [15], prescreening of handwritten notes [20], or the creation of digital libraries of historical documents [8]. After several decades of ongoing research, however, off-line handwritten text recognition is still considered a difficult problem that is only partially solved [4,17].…”

Section: Introductionmentioning

confidence: 99%

Self-training for Handwritten Text Line Recognition

Frinken

Bunke

2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Off-line handwriting recognition deals with the task of automatically recognizing handwritten text from images, for example from scanned sheets of paper. Due to the tremendous variations of writing styles encountered between different individuals, this is a very challenging task. Traditionally, a recognition system is trained by using a large corpus of handwritten text that has to be transcribed manually. This, however, is a laborious and costly process. Recent developments have proposed semisupervised learning, which reduces the need for manually transcribed text by adding large amounts of handwritten text without transcription to the training set. The current paper is the first one, to the knowledge of the authors, where semi-supervised learning for unconstrained handwritten text line recognition is proposed. We demonstrate the applicability of selftraining, a form of semi-supervised learning, to neural network based handwriting recognition. Through a set of experiments we show that text without transcription can successfully be used to significantly increase the performance of a handwriting recognition system.

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Section: Introductionmentioning

confidence: 99%

Self-training for Handwritten Text Line Recognition

Frinken

Bunke

2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…It has been a popular area of research for a few decades under the purview of pattern recognition and image processing [4] - [7]. A fundamental challenge in automated handwriting recognition is that recognition problems that appear to be simple for most people may in fact be quite difficult when transferred to machine domain [8], [9].Despite over five decades of intensive research, handwriting recognition continues to be an active area of research because of many unsolved fundamental theoretical problems as well as a rapidly increasing number of applications that can benefit from it [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

F.Ajao¹,

O.Olabiyisi²,

O.Omidiora³

et al. 2015

IJAIS

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This paper presents an approach to evaluate the quality of handwritten words using the set of features in the preprocessing stages. This is to determine the effect of various stages of preprocessing of the recognition of Yoruba handwritten characters. We demonstrate our methods using handwritten words samples of the domain of Yoruba medical terminology collected from indigenous Yoruba literate writers. Samples were digitized at 300dpi to facilitate much detail representation of the image dataset. We study the impact of entropy measure for an intuitive interpretation of the analysis of the handwritten words. From the experiment carried out, it was observed that the entropy measure of handwritten word is higher than the typewritten word. This implies that the Information content of the handwritten word is affected by perturbations which need to be removed using appropriate image preprocessing tools to obtain low entropy measure which implies same information content as the original. General TermsPattern Recognition, Handwritten word recognition, Image preprocessing and Information Theory.

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“…There exist many research works towards handwritten word recognition in Roman [1,6,17], Japanese/Chinese [2,3] and Arabic scripts [5]. To overcome the drawbacks of recognition approaches, word spotting technique [11,14,23,25] is used for information retrieval purpose.…”

Section: Introductionmentioning

confidence: 99%

Cross-language framework for word recognition and spotting of Indic scripts

Bhunia

Roy

Mohta

et al. 2018

Pattern Recognition

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Handwritten word recognition and spotting of low-resource scripts are difficult as sufficient training data is not available and it is often expensive for collecting data of such scripts. This paper presents a novel cross language platform for handwritten word recognition and spotting for such low-resource scripts where training is performed with a sufficiently large dataset of an available script (considered as source script) and testing is done on other scripts (considered as target script). Training with one source script and testing with another script to have a reasonable result is not easy in handwriting domain due to the complex nature of handwriting variability among scripts. Also it is difficult in mapping between source and target characters when they appear in cursive word images. The proposed Indic cross language framework exploits a large resource of dataset for training and uses it for recognizing and spotting text of other target scripts where sufficient amount of training data is not available. Since, Indic scripts are mostly written in 3 zones, namely, upper, middle and lower, we employ zone-wise character (or component) mapping for efficient learning purpose. The performance of our crosslanguage framework depends on the extent of similarity between the source and target scripts. Hence, we devise an entropy based script similarity score using source to target character mapping that will provide a feasibility of cross language transcription. We have tested our approach in three Indic scripts, namely, Bangla, Devanagari and Gurumukhi, and the corresponding results are reported.

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Recognition of cursive Roman handwriting: past, present and future

Cited by 98 publications

References 111 publications

Self-training for Handwritten Text Line Recognition

Self-training for Handwritten Text Line Recognition

Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

Cross-language framework for word recognition and spotting of Indic scripts

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