Improved Handwriting Recognition by Combining Two Forms of Hidden Markov Models and a Recurrent Neural Network

Frinken, Volkmar; Peter, Tim; Fischer, Andreas; Bunke, Horst; Do, Trinh-Minh-Tri; Artières, Thierry

doi:10.1007/978-3-642-03767-2_23

Cited by 19 publications

(5 citation statements)

References 14 publications

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“…In [47], a combination of HMMs, maximum margin HMMs, and BLSTM was investigated. Combination methods include averaging, maximum, Borda count, and weighted and unweighted voting.…”

Section: Decision Fusion For Handwriting Recognitionmentioning

confidence: 99%

Combining RtL and LtR HMMs to recognise handwritten Farsi words of small‐ and medium‐sized vocabularies

Arani

Kabir

Ebrahimpour

2018

IET Computer Vision

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In this study, a method for holistic recognition of handwritten Farsi words is proposed, which fuses the outputs of right-to-left (RtL) and left-to-right (LtR) hidden Markov models (HMMs). The experimental results on 16,000 images of 200 names of Iranian cities, from the 'Iranshahr 3' are presented and compared with those methods using only RtL or LtR models. Experimental results show that the main sources of error are similar beginnings or similar endings of the words. Since RtL and LtR models when dealing with the words behave differently, there is notable error diversity between the two classifiers in such a way that their combination increases the recognition rate. Compared to the RtL-HMM, the product of output scores of the RtL and LtR-HMMs reduces the classification error to about 6, 6 and 3%, for three different feature sets. A subjective error analysis on the results is also provided.

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“…In [47], a combination of HMMs, maximum margin HMMs, and BLSTM was investigated. Combination methods include averaging, maximum, Borda count, and weighted and unweighted voting.…”

Section: Decision Fusion For Handwriting Recognitionmentioning

confidence: 99%

Combining RtL and LtR HMMs to recognise handwritten Farsi words of small‐ and medium‐sized vocabularies

Arani

Kabir

Ebrahimpour

2018

IET Computer Vision

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“…Frinken et al 8 designed a scheme to combine OCR results from Hidden Markov Model (HMM) and recurrent neural network to improve the handwriting recognition accuracy. In order to avoid errors introduced from incorrect character segmentation, Wilczok et al 9 used geometrical information from different segmentation approaches to synchronize strings from multiple commercial OCR systems to achieve 1 This paper is based upon work supported by the DARPA MADCAT Program.…”

Section: Introductionmentioning

confidence: 99%

Boost OCR accuracy using iVector based system combination approach

2015

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Optical character recognition (OCR) is a challenging task because most existing preprocessing approaches are sensitive to writing style, writing material, noises and image resolution. Thus, a single recognition system cannot address all factors of real document images. In this paper, we describe an approach to combine diverse recognition systems by using iVector based features, which is a newly developed method in the field of speaker verification. Prior to system combination, document images are preprocessed and text line images are extracted with different approaches for each system, where iVector is transformed from a high-dimensional supervector of each text line and is used to predict the accuracy of OCR. We merge hypotheses from multiple recognition systems according to the overlap ratio and the predicted OCR score of text line images. We present evaluation results on an Arabic document database where the proposed method is compared against the single best OCR system using word error rate (WER) metric.

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“…Furthermore, by imposing different thresholds on a machine-generated score, different discrete classifiers can be obtained. Multiple candidate classifiers can be generated by extracting different features [4] or by utilising different modelling techniques [5].…”

Section: Introductionmentioning

confidence: 99%

Optimal human-machine collaboration for enhanced cost-sensitive biometric authentication

Coetzer

Swanepoel

Sabourin³

2021

SAIEE Afr. Res. J.

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Despite growing interest in human-machine collaboration for enhanced decision-making, little work has been done on the optimal fusion of human and machine decisions for cost-sensitive biometric authentication. An elegant and robust protocol for achieving this objective is proposed. The merits of the protocol is illustrated by simulating a scenario where a workforce of human experts and a score-generating machine are available for the authentication of handwritten signatures on, for example, bank cheques. The authentication of each transaction is determined by its monetary value and the quality of the claimed author's signature. A database with 765 signatures is considered, and an experiment that involves 24 human volunteers and two different machines is conducted. When a reasonable number of experts are kept in the loop, the average expected cost associated with the workforce-machine hybrid is invariably lower than that of the unaided workforce and that of the unaided machine.

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Improved Handwriting Recognition by Combining Two Forms of Hidden Markov Models and a Recurrent Neural Network

Cited by 19 publications

References 14 publications

Combining RtL and LtR HMMs to recognise handwritten Farsi words of small‐ and medium‐sized vocabularies

Combining RtL and LtR HMMs to recognise handwritten Farsi words of small‐ and medium‐sized vocabularies

Boost OCR accuracy using iVector based system combination approach

Optimal human-machine collaboration for enhanced cost-sensitive biometric authentication

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