Sub-word-based Offline Handwritten Farsi Word Recognition Using Recurrent Neural Network

Ghadikolaie, Mohammad Fazel Younessy; Kabir, Ehsanollah; Razzazi, Farbod

doi:10.4218/etrij.16.0115.0542

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…Linguistic fuzzy models have demonstrated robustness to Persian script manuscripts variations [ 28 ]. More recently, RNN and Deep NN has been introduced along with new segmentation techniques [ 29 ] or architectures such as DensNet and Xception [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach

Bergamaschi

Nardis²,

Ruozzi

et al. 2022

Sensors

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The linguistic and social impact of multiculturalism can no longer be neglected in any sector, creating the urgent need of creating systems and procedures for managing and sharing cultural heritages in both supranational and multi-literate contexts. In order to achieve this goal, text sensing appears to be one of the most crucial research areas. The long-term objective of the DigitalMaktaba project, born from interdisciplinary collaboration between computer scientists, historians, librarians, engineers and linguists, is to establish procedures for the creation, management and cataloguing of archival heritage in non-Latin alphabets. In this paper, we discuss the currently ongoing design of an innovative workflow and tool in the area of text sensing, for the automatic extraction of knowledge and cataloguing of documents written in non-Latin languages (Arabic, Persian and Azerbaijani). The current prototype leverages different OCR, text processing and information extraction techniques in order to provide both a highly accurate extracted text and rich metadata content (including automatically identified cataloguing metadata), overcoming typical limitations of current state of the art approaches. The initial tests provide promising results. The paper includes a discussion of future steps (e.g., AI-based techniques further leveraging the extracted data/metadata and making the system learn from user feedback) and of the many foreseen advantages of this research, both from a technical and a broader cultural-preservation and sharing point of view.

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Section: Related Workmentioning

confidence: 99%

Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach

Bergamaschi

Nardis²,

Ruozzi

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…e encoder maps the input data x ∈ [0, 1] n+1 onto a latent coded space y ∈ [0, 1] m+1 , in which m is smaller than n most of the time. e encoder output is obtained through the following equation [53,54]:…”

Section: 2mentioning

confidence: 99%

“…e autoencoder tries to converge x on x by adjusting weights and biases. e di erence between x and x is called the loss function, the value of which can be minimized to train the network [54].…”

Section: 2mentioning

confidence: 99%

Recognition of Persian/Arabic Handwritten Words Using a Combination of Convolutional Neural Networks and Autoencoder (AECNN)

Khosravi

Chalechale

2022

Mathematical Problems in Engineering

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Despite extensive research, recognition of Persian and Arabic manuscripts is still a challenging problem due to the complicated and irregular nature of writing, wide vocabulary, and diversity of handwritings. In Persian and Arabic words, letters are joined together, and signs such as dots are placed above or below letters. In the proposed approach, the words are first decomposed into their constituent subwords to enhance the recognition accuracy. Then the signs of subwords are extracted to develop a dictionary of main subwords and signs. The dictionary is then employed to train a classifier. Since the proposed recognition approach is based on unsigned subwords, the classifier may make a mistake in recognizing some subwords of a word. To overcome this, a new subword fusion algorithm is proposed based on the similarity of the main subwords and signs. Here, convolutional neural networks (CNNs) are utilized to train the classifier. An autoencoder (AE) network is employed to extract appropriate features. Thus, a hybrid network is developed and named AECNN. The known Iranshahr dataset, including nearly 17000 images of handwritten names of 503 cities of Iran, was employed to analyze and test the proposed approach. The resultant recognition accuracy is 91.09%. Therefore, the proposed approach is much more capable than the other methods known in the literature.

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“…The best accuracy among the traditional classifiers based on all investigated features is 99.3% accuracy obtained by the SVM, and the CNN achieves the best overall accuracy of 99.45% [18]. Gadikolaie et al [19] presented a segmentaion-based method for offline Farsi handwritten word recognition. They segmented the words into sub-words.…”

Section: ) Literature Reviewmentioning

confidence: 99%

Persian handwritten digit, character and word recognition using deep learning

Bonyani

Jahangard

Daneshmand

2021

IJDAR

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Digit, character, and word recognition of a particular script play a key role in the field of pattern recognition. These days, Optical Character Recognition (OCR) systems are widely used in commercial market in various applications. In recent years, there are intensive research studies on optical character, digit, and word recognition. However, only a limited number of works are offered for numeral, character, and word recognition of Persian scripts. In this paper, we have used deep neural network and investigated different versions of DensNet models and Xception and compare our results with the state-of-the-art methods and approaches in recognizing Persian character, number, and word. Two holistic Persian handwritten datasets, HODA and Sadri, have been used. For a comparison of our proposed deep neural network with previously published research studies, the best state-of-the-art results have been considered. We used accuracy as our criteria for evaluation. For HODA dataset, we achieved 99.72% and 89.99% for digit and character, respectively. For Sadri dataset, we obtained accuracy rates of 99.72%, 98.32%, and 98.82% for digit, character, and words, respectively.

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Sub-word-based Offline Handwritten Farsi Word Recognition Using Recurrent Neural Network

Cited by 10 publications

References 21 publications

Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach

Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach

Recognition of Persian/Arabic Handwritten Words Using a Combination of Convolutional Neural Networks and Autoencoder (AECNN)

Persian handwritten digit, character and word recognition using deep learning

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