Classification of Urdu Ligatures Using Convolutional Neural Networks - A Novel Approach

Javed, Nizwa; Shabbir, Safia; Siddiqi, Imran; Khurshid, Khurram

doi:10.1109/fit.2017.00024

Cited by 18 publications

(7 citation statements)

References 19 publications

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“…They reported good results on both CLE and UPTI. A similar kind of study on 38K ligatures images for 98 classes scanned from the book, was done by Javed et al [61], they used fixedsize ligatures of 55 × 55 pixels and reported an accuracy of 95%. Also, Rafeeq et al [21] showed that a deep neural network and clustering of ligatures significantly enhances the classification accuracy on the custom generated a dataset of 17010 Urdu ligatures from CLE [22] in 2018.…”

Section: Hybrid/end-to-end Approachesmentioning

confidence: 80%

Two Stream Deep Neural Network for Sequence-Based Urdu Ligature Recognition

Arafat

Iqbal

2019

IEEE Access

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Urdu text is a complex cursive script and poses a challenge for recognition by OCR systems due to its large number of ligatures and cursive style. In literature, several techniques have been proposed to recognize Urdu ligatures. However, we have investigated that, suitable challenging datasets and the consequently higher recognition rate is needed for ligature recognition. In this paper, a hybrid model based on the holistic approach is adopted for the recognition of Urdu ligatures (compound characters). More than 3800 unique ligatures were used to generate 46K (38K training, 7K testing) synthetic ligatures with 9 different kinds of transformations along with the normal ligatures. Each ligature is processed through two streams of Deep Neural Networks, namely Alexnet and Vgg16 to obtain a unique set of features corresponding to each net. These features are fused and then used as an input to double layer Bidirectional Long Short Term (BLSTM) network for learning a model. The learned model maps ligature images to their corresponding sequence of individual Urdu characters. In the proposed methodology output is in the editable Urdu-script format. The proposed model was evaluated and have shown an accuracy of 97% on the training dataset and 80% on more than 7K parametrically different query ligatures (test-set).

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Section: Hybrid/end-to-end Approachesmentioning

confidence: 80%

Two Stream Deep Neural Network for Sequence-Based Urdu Ligature Recognition

Arafat

Iqbal

2019

IEEE Access

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“…They developed four separate recognizers to accommodate varied text sizes for this objective. Reference [10] suggested CNN recognize Nastaleeq font style ligatures. The technique analyzed 18,000 ligatures over 98 classes and achieved a recognition rate of 95%.…”

Section: Holistic Approachmentioning

confidence: 99%

Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

Tayyab¹,

Hussain²,

Mir³

et al. 2023

Computers, Materials &Amp; Continua

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The news ticker is a common feature of many different news networks that display headlines and other information. News ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory authorities. In this paper, we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news channel. The primary emphasis of this research is on ticker recognition methods and storage schemes. To that end, the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification method. The proposed learning architecture considers the grouping of homogeneousshaped classes. This incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual biases. Furthermore, experiments with a novel Arabic News Ticker (Al-ENT) dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested approach. The proposed method attains 96.5%, outperforming the current state-of-the-art technique by 8.5%. The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.

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“…Among different identification techniques, Sabbour and Shafait in [11] use object descriptors to recognize ligatures in the UPTI database, resulting in an 89% recognition rate. On the UPTI database [12][13][14], many implicit partition-based techniques have also been calculated. In such methods, the learning limitations are determined after the ground truth transcription, and the pictures of text lines are provided to the learning algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Layer Holistic Approach for Cursive Text Recognition

et al. 2022

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Urdu is a widely spoken and narrated language in several South-Asian countries and communities worldwide. It is relatively hard to recognize Urdu text compared to other languages due to its cursive writing style. The Urdu text script belongs to a non-Latin cursive family script like Arabic, Hindi and Chinese. Urdu is written in several writing styles, among which `Nastaleeq’ is the most popular and widely used font style. A gap still poses a challenge for localization/detection and recognition of Urdu Nastaleeq text as it follows modified version of Arabic script. This research study presents a methodology to recognize and classify Urdu text in Nastaleeq font, regardless of the text position in the image. The proposed solution is comprised of a two-step methodology. In the first step, text detection is performed using the Connected Component Analysis (CCA) and Long Short-Term Memory Neural Network (LSTM). In the second step, a hybrid Convolution Neural Network and Recurrent Neural Network (CNN-RNN) architecture is deployed to recognize the detected text. The image containing Urdu text is binarized and segmented to produce a single-line text image fed to the hybrid CNN-RNN model, which recognizes the text and saves it in a text file. The proposed technique outperforms the existing ones by achieving an overall accuracy of 97.47%.

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Classification of Urdu Ligatures Using Convolutional Neural Networks - A Novel Approach

Cited by 18 publications

References 19 publications

Two Stream Deep Neural Network for Sequence-Based Urdu Ligature Recognition

Two Stream Deep Neural Network for Sequence-Based Urdu Ligature Recognition

Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

A Multi-Layer Holistic Approach for Cursive Text Recognition

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