Deep recurrent neural networks with word embeddings for Urdu named entity recognition

Khan, Wahab; Daud, Ali; Alotaibi, Fahd Saleh; Aljohani, Naif Radi; Arafat, Sachi

doi:10.4218/etrij.2018-0553

Cited by 28 publications

(23 citation statements)

References 12 publications

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“…It includes building end-to-end OCR systems for English, as well as for other languages. The key reason for these groundbreaking results stem from the multi-layer trait of deep neural networks [57]. One such layered architecture consists of CNN layers which are followed by RNN layers.…”

Section: B Deep Learning For Ocrmentioning

confidence: 99%

MMU-OCR-21: Towards End-to-End Urdu Text Recognition Using Deep Learning

Nasir¹,

Malik

Shahzad

2021

IEEE Access

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Optical Character Recognition (OCR) is a technique that generates text from an image. Recognizing the importance of OCR in real-world settings, a plethora of techniques have been developed for Western, as well as Asian languages. Urdu is a prominent South Asian language and a number of different solutions for Urdu OCR have been proposed. However, fewer attempts have been made to develop end-to-end deep learning-based solutions for recognizing printed Urdu text. Furthermore, several benchmark corpora for Urdu OCR have been developed that can be used for training and evaluation of different OCR techniques. However, there are a number of limitations of the existing Urdu corpora: firstly, most of them have either character or word or text images, which are usually rendered using only a single font, Nastaleeq. Secondly, the volume of the existing datasets is so small that it is not suitable for working with the deep-learning techniques that have achieved groundbreaking results for OCRs. To that end, in this study, we have proposed a very large Multi-level and Multi-script Urdu corpus (MMU-OCR-21). It is the largest-ever Urdu corpus of printed text that is effectively suitable to work with deep learning techniques. In total, the corpus is composed of over 602,472 images, including text-line and word images in three prominent fonts, and their respective ground truth. Also, we have performed experiments using multiple state-of-the-art deep learning techniques for text-line and word level images.

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Section: B Deep Learning For Ocrmentioning

confidence: 99%

MMU-OCR-21: Towards End-to-End Urdu Text Recognition Using Deep Learning

Nasir¹,

Malik

Shahzad

2021

IEEE Access

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“…This kind of task is achieved by named-entity recognition. NER is a subtask in information extraction and machine translation and also various DRNN (Deep Recurrent Neural Network) models along with word embedding are applied to perform NER [18]. With the help of NER, we can identify and categorize key entities in text which will help our chatbots to become more interactive.…”

Section: Named-entity Recognitionmentioning

confidence: 99%

A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot

Abedin¹,

Al²,

Nowrin³

et al. 2021

Natural Language Processing

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In recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second. Though chatbots perform well in task-oriented activities, in most cases they fail to understand personalized opinions, statements or even queries which later impact the organization for poor service management. Lack of understanding capabilities in bots disinterest humans to continue conversations with them. Usually, chatbots give absurd responses when they are unable to interpret a user’s text accurately. Extracting the client reviews from conversations by using chatbots, organizations can reduce the major gap of understanding between the users and the chatbot and improve their quality of products and services.Thus, in our research we incorporated all the key elements that are necessary for a chatbot to analyse andunderstand an input text precisely and accurately. We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence. The efficiency of our approach can be demonstrated accordingly by the detailed analysis.

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“…Some of the researchers developed a novel approach for the character recognition models like Sara et al, [1] proposed spatial-temporal based features for the recognition of cursive text in Arabic/Persian languages. Rafeeq et al, [2] and Khan et al, [3] proposed the concepts of a deep neural network for the recognition of Urdu ligatures. While some researchers like Hussain et al, [4] and Tagougui et al, [5] worked on the existing techniques and presented survey papers for addressing the limitations in the available studies.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Pashto Characters Recognition

Khan

Nazir

2022

PPASA

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In artificial intelligence, text identification and analysis that are based on images play a vital role in the text retrieving process. Automatic text recognition system development is a difficult task in machine learning, but in the case of cursive languages, it poses a big challenge to the research community due to slight changes in character’s shapes and the unavailability of a standard dataset. While this recognition task becomes more challenging in the case of Pashto language due to a large number of characters in its dataset than other similar cursive languages (Persian, Urdu, Arabic) and a slight change in character’s shape. This paper aims to address accept these challenges by developing an optimal optical character recognition (OCR) system to recognise isolated handwritten Pashto characters. The proposed OCR system is developed using multiple long short-term memory (LSTM) based deep learning model. The applicability of the proposed model is validated by using the decision trees (DT) classification tool based on the zoning feature extraction technique and the invariant moment approaches. An overall accuracy rate of 89.03% is calculated for the multiple LSTM-based OCR system while DT-based recognition rate of 72.9% is achieved using zoning feature vector and 74.56% is achieved for invariant moments-based feature map. Applicability of the system is evaluated using different performance metrics of accuracy, f-score, specificity, and varying training and test sets.

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Deep recurrent neural networks with word embeddings for Urdu named entity recognition

Cited by 28 publications

References 12 publications

MMU-OCR-21: Towards End-to-End Urdu Text Recognition Using Deep Learning

MMU-OCR-21: Towards End-to-End Urdu Text Recognition Using Deep Learning

A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot

Deep Learning Based Pashto Characters Recognition

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