Multi-font printed Mongolian document recognition system

Peng, Liangrui; Liu, Changsong; Ding, Xiaoqing; Jin, Jian‐Ming; Wu, Yulin; Wang, Hua; Yanhua, Bao

doi:10.1007/s10032-009-0106-8

Cited by 23 publications

(3 citation statements)

References 20 publications

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“…By integrating the acupuncture effect, hyperthermia effect, and acupoint specific stimulation, MMA regulates the body and treats diseases by a complex multi-system, multi-channel, and multi-path mechanism including blood circulation, nervous system and immune functions. Meanwhile, MMA evidently changes monoamine neurotransmitters and the expressioncontents of certain cytokines in exercise-induced fatigue rat models [15][16][17][18][19]. Currently, the action mechanism of MMA on insomnia remains unclear.…”

Section: Page 5 Of 22mentioning

confidence: 99%

The effect of Mongolian medical acupuncture on cytokines and neurotransmitters in the brain tissue of insomniac rats

Wang

Wuyun

et al. 2015

European Journal of Integrative Medicine

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Section: Page 5 Of 22mentioning

confidence: 99%

The effect of Mongolian medical acupuncture on cytokines and neurotransmitters in the brain tissue of insomniac rats

Wang

Wuyun

et al. 2015

European Journal of Integrative Medicine

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“…The segmentation method was adopted in the earliest Mongolian OCR. In the first step, the glyph is segmented from the image, then the feature of each glyph is extracted, and finally the glyph is classified and recognized by matching with the template 4 . In short, Mongolian characters are seamlessly connected, so the segmentation of glyphs is a difficult and challenging task.…”

Section: Related Workmentioning

confidence: 99%

A new dataset for mongolian online handwritten recognition

Pan

Fan

et al. 2023

Sci Rep

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This paper introduces a new traditional Mongolian word-level online handwriting dataset, MOLHW. The dataset consists of handwritten Mongolian words, including 164,631 samples written by 200 writers and covering 40,605 Mongolian common words. These words were selected from a large Mongolian corpus. The coordinate points of words were collected by volunteers, who wrote the corresponding words on the dedicated application for their mobile phones. Latin transliteration of Mongolian was used to annotate the coordinates of each word. At the same time, the writer’s identification number and mobile phone screen information were recorded in the dataset. Using this dataset, we propose an encoder–decoder Mongolian online handwriting recognition model with a deep bidirectional gated recurrent unit and attention mechanism as the baseline evaluation model. Under this model, the optimal performance of the word error rate (WER) on the test set was 24.281%. Furthermore, we present the experimental results of different Mongolian online handwriting recognition models. The experimental results show that compared with other models, the model based on Transformer could learn the corresponding character sequences from the coordinate data of the dataset more effectively, with a 16.969% WER on the test set. The dataset is now freely available to researchers worldwide. The dataset can be applied to handwritten text recognition as well as handwritten text generation, handwriting identification, and signature recognition.

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“…For the Mongolian language, a multi-font recognition technique was suggested by Peng et al [15]. The suggested technique used an analytical method that utilized the properties of projection profiles and linked its elements for determining segmentation points.…”

Section: Related Workmentioning

confidence: 99%

A Scale and Rotation Invariant Urdu Nastalique Ligature Recognition Using Cascade Forward Backpropagation Neural Network

Rehman

Khan

2019

IEEE Access

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In the emerging age of technologies, machines are becoming more and more skilled and capable just like humans. Despite the fact that machines do not have their own intelligence, but still due to advancement in Artificial Intelligence (AI), machines are rapidly advancing. The area of Pattern Recognition (PR) deals with bringing enhancements to identify obscure patterns corresponding to specific classes. Optical Character Recognition (OCR) is a subfield of PR which deals with the recognition of characters. A great work has been done for Japanese, Hindi, Arabic and Chinese scripts, but only a diminutive work has been done for Urdu script. The Urdu language is highly cursive and is written in different calligraphic styles like Naskh, Nastalique, Kofi, Devani and Riqa. The Nastalique font is very calligraphic with aesthetic beauty. The ligature segmentation of Urdu Nastalique is also more difficult as compared to other languages. Urdu Nastalique has some characteristics like stacking of ligatures and cursiveness which makes its ligature segmentation a difficult task. Cursiveness means ligatures are joined together to form a new shape. It contains connected ligatures which makes it more complicated as compared to other languages. The ligature recognition of Urdu text by an OCR is a strenuous task due to variants of scaling, rotation, orientation and font style. In this study, a scale and rotation invariant classifier for Urdu Nastalique OCR is proposed. A combination of scale and location invariant moments is used for feature extraction and the classification is performed using Cascade Forward Backpropagation Neural Network. The model is validated through independent dataset testing and 5-fold cross-validation which gave 96.474% and 96.922% accuracy. The results depict the adaptability of the proposed model due to its high accuracy for recognition of Urdu Nastalique Ligature.INDEX TERMS Deep neural network (DNN), optical character recognition (OCR), scale invariant classifier, rotation invariant classifier.

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Multi-font printed Mongolian document recognition system

Cited by 23 publications

References 20 publications

The effect of Mongolian medical acupuncture on cytokines and neurotransmitters in the brain tissue of insomniac rats

The effect of Mongolian medical acupuncture on cytokines and neurotransmitters in the brain tissue of insomniac rats

A new dataset for mongolian online handwritten recognition

A Scale and Rotation Invariant Urdu Nastalique Ligature Recognition Using Cascade Forward Backpropagation Neural Network

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