Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking

AlKhateeb, Jawad H.; Ren, Jinchang; Jiang, Jianmin; Al-Muhtaseb, Husni

doi:10.1016/j.patrec.2011.02.006

Cited by 106 publications

(50 citation statements)

References 30 publications

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“…Those words are 937 Tunisian town/village names. IFN/ENIT Database is divided in 5 sets (see Table 2) and it was successfully used by more than 50 research groups [16][17][18] as well in Offline Arabic handwriting recognition competition in ICDAR 2009 [19]. We train our system with 19724 words collected in seta, set b and setc, however we use set d and set e, that contains 12768 words, for testing.…”

Section: Experiments Resultsmentioning

confidence: 99%

Recognition of Handwritten Arabic Words with Dropout Applied in MDLSTM

Maalej

Kherallah

2016

Lecture Notes in Computer Science

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Abstract. Offline handwriting recognition is the ability to decode an intelligible handwritten input from paper documents into digitized format readable by machines. This field remains an on-going research problem especially for Arabic Script due to its cursive appearance, the variety of writers and the diversity of styles. In this paper we focus on the Intelligent Words Recognition system based on MDLSTM, on which a dropout technique is applied during training stage. This technique prevents our system against overfitting and improves the recognition rate. To evaluate our system we use IFN/ENIT database.

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Section: Experiments Resultsmentioning

confidence: 99%

Recognition of Handwritten Arabic Words with Dropout Applied in MDLSTM

Maalej

Kherallah

2016

Lecture Notes in Computer Science

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“…They extract several structural features and a group of intensity features using a sliding window. Experiments were carried out using the IFN/ENIT database which contains 32,492 handwritten Arabic words [3].Volker Märgner eta al presents the IfN's Offline Handwritten Arabic Word Recognition System. The system uses Hidden Markov Models (HMM) for word recognition, and is based on character recognition without explicit segmentation [4].…”

Section: Related Studiesmentioning

confidence: 99%

Holistic Approach for Arabic Word Recognition

Wahbi¹,

Musa²

2016

IJCATR

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Abstract. Optical Character Recognition (OCR) is one of the important branches. One segmenting words into character is one of the most challenging steps on OCR. As the results of advances in machine speeds and memory sizes as well as the availability of large training dataset, researchers currently study Holistic Approach "recognition of a word without segmentation". This paper describes a method to recognize off-line handwritten Arabic names. The classification approach is based on Hidden Markov models.. For each Arabic word many HMM models with different number of states have been trained. The experiments result are encouraging, it also show that best number of state for each word need careful selection and considerations.

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“…Features extraction is a very important process in every OCR system; a lot of techniques have been adopted by searchers [8,9,21,37,55], some of which are: geometrical features (moments, histograms, and direction features), structural features (line element features, Fourier descriptors, topological features) and transformation methods [principal The last phase of a OCRS is building a recognizer. This stage is achieved in two steps.…”

Section: Related Workmentioning

confidence: 99%

A novel fuzzy approach for handwritten Arabic character recognition

Kef

Chergui

Chikhi

2015

Pattern Anal Applic

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The aim of our work is to present a new method based on structural characteristics and a fuzzy classifier for off-line recognition of handwritten Arabic characters in all their forms (beginning, end, middle and isolated). The proposed method can be integrated in any handwritten Arabic words recognition system based on an explicit segmentation process. First, three preprocessing operations are applied on character images: thinning, contour tracing and connected components detection. These operations extract structural characteristics used to divide the set of characters into five subsets. Next, features are extracted using invariant pseudo-Zernike moments. Classification was done using the Fuzzy ARTMAP neural network, which is very fast in training and supports incremental learning. Five Fuzzy ARTMAP neural networks were employed; each one is designed to recognize one subset of characters. The recognition process is achieved in two steps: in the first one, a clustering method affects characters to one of the five character subsets. In the second one, the pseudo-Zernike features are used by the appropriate Fuzzy ARTMAP classifier to identify the character. Training process and tests were performed on a set of character images manually extracted from the IFN/ENIT database. A height recognition rate was reported.

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Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking

Cited by 106 publications

References 30 publications

Recognition of Handwritten Arabic Words with Dropout Applied in MDLSTM

Recognition of Handwritten Arabic Words with Dropout Applied in MDLSTM

Holistic Approach for Arabic Word Recognition

A novel fuzzy approach for handwritten Arabic character recognition

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