Analysis of Preprocessing Techniques for Latin Handwriting Recognition

Pesch, Hendrik; Hamdani, Mahdi; Förster, Jens; Ney, Hermann

doi:10.1109/icfhr.2012.179

Cited by 19 publications

(13 citation statements)

References 11 publications

(18 reference statements)

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“…7.4 for the IAMDB and CS corpora, with the exception of feature extraction, and some details of training and parameter optimization details. Feature extraction for PARZI-VAL and GW were carried out as described in [41]. The PARZIVAL bi-gram LM was trained using only the transcripts from its training and validation partitions.…”

Section: Additional Results and Comparisonsmentioning

confidence: 99%

HMM word graph based keyword spotting in handwritten document images

Toselli

Vidal

Romero

et al. 2016

Information Sciences

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Line-level keyword spotting (KWS) is presented on the basis of frame-level word posterior probabilities. These posteriors are obtained using word graphs derived from the recognition process of a full-fledged handwritten text recognizer based on hidden Markov models and N-gram language models. This approach has several advantages. First, since it uses a holistic, segmentation-free technology, it does not require any kind of word or character segmentation. Second, the use of language models allows the context of each spotted word to be taken into account, thereby considerably increasing the KWS accuracy. And third, the proposed KWS scores are based on true posterior probabilities, computed taking into account all (or most) possible word segmentations of the input image. These scores are properly bounded and normalized. This mathematically clean formulation lends itself to smooth, threshold-based keyword queries which, in turn, permit comfortable trade-offs between search precision and recall. Experiments are carried out on several historic collections of handwritten text images, as well as with a well-known dataset of modern English handwritten text. According to the empirical results, the proposed approach achieves KWS results comparable to those obtained with the recently-introduced "BLSTM neural networks KWS" approach and clearly outperform the popular, state-of-the-art "Filler HMM" KWS method. Overall, the results clearly support all the above-claimed advantages of the proposed approach.

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Section: Additional Results and Comparisonsmentioning

confidence: 99%

HMM word graph based keyword spotting in handwritten document images

Toselli

Vidal

Romero

et al. 2016

Information Sciences

View full text Add to dashboard Cite

show abstract

“…In Reference [15], the contribution of preprocessing methods were analyzed; the contrast normalization, the slant correction, the size normalization, and the Median filter are used as preprocessing methods.…”

Section: Of 25mentioning

confidence: 99%

Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

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One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.

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“…Chinese [27,46], Arabic [16], and Latin [32]), characters [1], or digits/numbers [4], and even context specific handwriting recognition (e.g. dates and checks [18]).…”

Section: Handwritingmentioning

confidence: 99%