Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition

Bianne-Bernard, Anne-Laure; Menasri, F.; Likforman-Sulem, Laurence; Mokbel, Chafic; Kermorvant, Christopher

doi:10.1117/12.912093

Cited by 7 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The features extraction method used in our system is inspired by work of El-Hajj (E.L-Hajj et al, 2009) with some modifications, the used technique has shown excellent results in several researches (Bianne-bernard et al, 2009;Bluche et al, 2015). The features extraction stage consists of extracting a sequence of characteristics vector by dividing the word image into vertical frames.…”

Section: Extraction Featuresmentioning

confidence: 99%

Recognition of cursive Arabic handwritten text using embedded training based on HMMs

Rabi

Amrouch

Mahani

2018

Journal of Electrical Systems and Information Technology

View full text Add to dashboard Cite

In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.

show abstract

Section: Extraction Featuresmentioning

confidence: 99%

Recognition of cursive Arabic handwritten text using embedded training based on HMMs

Rabi

Amrouch

Mahani

2018

Journal of Electrical Systems and Information Technology

View full text Add to dashboard Cite

show abstract

“…Since the foremost research was done in mid-1990s [1] [2] [5], the hidden Markov model (HMM) has become the state of the art offline handwriting recognition technique for alphabetic scripts such as Arabic, English and French [3] [4] [8] [6] [9] and French [6] [10]. The HMM provides a stochastic model for unified character segmentation and recognition.…”

Section: Introductionmentioning

confidence: 99%

Progress in the Raytheon BBN Arabic Offline Handwriting Recognition System

Cao

Natarajan

Peng

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

View full text Add to dashboard Cite

This paper presents the most recent progress and state of the art result obtained from BBN's Arabic offline handwriting recognition research. Our system is based a left-toright hidden Markov model and integrates discriminative learning methods including discriminative MPE and n-best rescoring using the scores of glyph classifiers (SVM, DNN) and the RNNLM. Arabic-related features for n-best rescoring are also investigated in this paper. Multi-stage MAP/MLLR and writer verification are applied to adapt the recognizer in all training situations. Consensus network is extensively researched for system combination and improving challenging preprocessing problems. 123

show abstract

“…Although a number of investigations have been made towards the recognition of isolated handwritten characters and digits of Indian scripts [8,39], only a few pieces of work [7,8,17,20] exist towards offline handwritten word recognition in Indian scripts. Recognition of Indian scripts [22,38] is difficult due to their complex syntax and spatial variation of the characters when combined with other characters to form a word.…”

Section: Introductionmentioning

confidence: 99%

“…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

View full text Add to dashboard Cite

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.

show abstract

Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition

Cited by 7 publications

References 0 publications

Recognition of cursive Arabic handwritten text using embedded training based on HMMs

Recognition of cursive Arabic handwritten text using embedded training based on HMMs

Progress in the Raytheon BBN Arabic Offline Handwriting Recognition System

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

Contact Info

Product

Resources

About