Off-line handwritten word recognition using multi-stream hidden Markov models

Kessentini, Yousri; Paquet, Thierry; Hamadou, Abdelmajid Ben

doi:10.1016/j.patrec.2009.08.009

Cited by 90 publications

(51 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…All filled forms were digitized at 300 dpi and stored as binary images. The database mainly targets preprocessing [118][119][120] and recognition of Arabic handwritten words [121][122][123][124][125][126] but has also been employed to evaluate writer identification systems [127][128][129]. Figure 5 illustrates a town name from the database written by 12 different writers.…”

Section: Ifn/enitmentioning

confidence: 99%

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

View full text Add to dashboard Cite

Handwriting has remained one of the most frequently occurring patterns that we come across in everyday life. Handwriting offers a number of interesting pattern classification problems including handwriting recognition, writer identification, signature verification, writer demographics classification and script recognition, etc. Research in these and similar related problems requires the availability of handwritten samples for validation of the developed techniques and algorithms. Like any other scientific domain, the handwriting recognition community has developed a large number of standard databases allowing development, evaluation and comparison of different techniques developed for a variety of recognition tasks. This paper is intended to provide a comprehensive survey of the handwriting databases developed during the last two decades. In addition to the statistics of the discussed databases, we also present a comparison of these databases on a number of dimensions. The ground truth information of the databases along with the supported tasks is also discussed. It is expected that this paper would not only allow researchers in handwriting recognition to objectively compare different databases but will also provide them the opportunity to select the most appropriate database(s) for evaluation of their developed systems.

show abstract

Section: Ifn/enitmentioning

confidence: 99%

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…Kessentini et al [20] presented a multi-stream approach for off-line handwritten word recognition. The proposed approach combines low level feature streams namely, density based features extracted from two different sliding windows with different widths, and contour based features extracted from upper and lower contours.…”

Section: Related Workmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…Today the field of machine learning and pattern recognition finds applications not only in the traditional fields like speech recognition [29,67] but also in new and emerging research areas (for example, isolated word recognition [23] using lip reading). Machine learning has also been used for many image processing applications such as image classification [56]; image segmentation [3,52], which is often used in many video and computer vision applications such as object localization/tracking/recognition, signal compression, and image retrieval [47]; image watermarking [54]; handwriting recognition [9]; age estimation from facial images [17]; object detection [59]; sketch recognition [69]; texture classification [75], etc. We refer the reader to [43,80] for comprehensive reviews of the applications of machine learning in image processing.…”

Section: Signal Processing Based Approach For Iqamentioning

confidence: 99%

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Narwaria

Wang

Çetin

2012

Pattern Recognition

View full text Add to dashboard Cite

a b s t r a c tMeasurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/ parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics.

show abstract

Off-line handwritten word recognition using multi-stream hidden Markov models

Cited by 90 publications

References 32 publications

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

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

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Contact Info

Product

Resources

About