Content-Aware Urdu Handwriting Generation

Memon, Zeeshan; Ul-Hasan, Adnan; Shafait, Faisal

doi:10.1007/978-3-031-41685-9_27

Cited by 2 publications

(6 citation statements)

References 14 publications

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“…For the Sudanese language, the historical "Sundanese Palm Leaf Manuscript" (HSPLM) dataset [78] was used by [54] for character recognition. For the Urdu language, the work of [52] utilized two databases: the "Center of Language Engineering" (CLE) [79] and UCOM [80]. The CLE database contains 18,000 Urdu ligatures in Unicode format, while the UCOM database comprises 48 distinct lines of Urdu text authored by 100 different writers.…”

Section: Recognition Tasks and Datasetsmentioning

confidence: 99%

“…Thus, two or more sets of document images that have good similarity in writing pattern can benefit from training a joint optical model. Even so, this approach was unusual and did not have much exploration as a data augmentation method, which only two selected studies used it [40,52,55].…”

Section: Transfer Learningmentioning

confidence: 99%

“…Recently, Memon et al [52] developed a handwriting generation model trained initially on ligatures images and later fine-tuned via transfer learning on handwritten images from the CLE to UCOM database. Their results showed significant progress, achieving a Fréchet Inception Distance (FID-the lower, the better) score of 38.03, a Geometry Score (GS-the lower, the better) of 8.81 × 10 −4 , and a recognition accuracy of 72.6%.…”

Section: Transfer Learningmentioning

confidence: 99%

“…Memon et al [52] discussed the challenges of recognizing multiple cursive scripts due to limited labeled training data. The work proposed a content-controlled training approach for Urdu handwriting generation combined with a pre-trained recognizer loss.…”

Section: Deep Learningmentioning

confidence: 99%

“…Transfer Learning was another field of study, but less explored (8.8%) [40,52,55], since it is not the main objective to use it as data augmentation. In any case, the challenge with this approach is to leverage the previous knowledge of the optical model to retrain it on another dataset.…”

Section: Rq3: What Is the Current State Of Data Augmentation Research...mentioning

confidence: 99%

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Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review

de Sousa Neto,

Bezerra,

de Moura

et al. 2024

SN COMPUT. SCI.

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Offline Handwritten Text Recognition (HTR) systems concern the automatic recognition and transcription of handwritten text from scanned images to digital media. Recently, HTR research field has become increasingly important due to the growing need for digitizing documents and automating data entry across various industries. However, achieving satisfactory results depend on the amount of available samples to train an optical model. Creating and labeling large enough datasets for this purpose often require significant time and effort, that in some situations may be impractical. To address this problem, data augmentation approaches are commonly used as an essential component of HTR systems. In this way, the present work aims to identify, explore, and analyze the scope of data augmentation approaches for offline HTR systems. Furthermore, we detailed our research protocol and answered four pertinent research questions, which enabled us to discuss trends and possible gaps. A search was conducted across five scientific databases, focusing on papers published between 2012 and 2023. The search yielded 976 primary papers, with 32 meeting the criteria for inclusion in this review. Our results indicate that handwriting synthesis is an emerging research field, and we observed that Digital Image Processing (DIP) is still widely used as an image generator. Nevertheless, the application of Generative Adversarial Networks (GAN) has gained traction in recent years owing to its impressive ability to synthesize images of handwritten text with arbitrary style and content. In addition, we explored and analyzed the most commonly used datasets and text recognition levels in the selected works.

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Section: Recognition Tasks and Datasetsmentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

Section: Rq3: What Is the Current State Of Data Augmentation Research...mentioning

confidence: 99%

See 3 more Smart Citations

Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review

de Sousa Neto,

Bezerra,

de Moura

et al. 2024

SN COMPUT. SCI.

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ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition

Hamza,

Ren,

Saeed

2024

PLoS ONE

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Automatic Urdu handwritten text recognition is a challenging task in the OCR industry. Unlike printed text, Urdu handwriting lacks a uniform font and structure. This lack of uniformity causes data inconsistencies and recognition issues. Different writing styles, cursive scripts, and limited data make Urdu text recognition a complicated task. Major languages, such as English, have experienced advances in automated recognition, whereas low-resource languages, such as Urdu, still lag. Transformer-based models are promising for automated recognition in high- and low-resource languages such as Urdu. This paper presents a transformer-based method called ET-Network that integrates self-attention into EfficientNet for feature extraction and a transformer for language modeling. The use of self-attention layers in EfficientNet helps to extract global and local features that capture long-range dependencies. These features proceeded into a vanilla transformer to generate text, and a prefix beam search is used for the finest outcome. NUST-UHWR, UPTI2.0, and MMU-OCR-21 are three datasets used to train and test the ET Network for a handwritten Urdu script. The ET-Network improved the character error rate by 4% and the word error rate by 1.55%, while establishing a new state-of-the-art character error rate of 5.27% and a word error rate of 19.09% for Urdu handwritten text.

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Content-Aware Urdu Handwriting Generation

Cited by 2 publications

References 14 publications

Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review

Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review

ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition

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