A survey on different feature extraction methods for writer identification and verification

Paul, Jaya; Dutta, Kalpita; Sarkar, Anasua; Das, Nibaran; Roy, Kaushik

doi:10.1504/ijapr.2023.130511

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Much more advanced Arabic OCR is required for this purpose. Additionally, this can be further extended semantically to differentiate writers through both the ability to recognize images and the meanings of words [80]. Moreover, the IoT can be integrated with OCR to provide a mechanism for signature verification [81]…”

mentioning

confidence: 99%

Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction

Najam

Faizullah

2023

Applied Sciences

View full text Add to dashboard Cite

Arabic handwritten-text recognition applies an OCR technique and then a text-correction technique to extract the text within an image correctly. Deep learning is a current paradigm utilized in OCR techniques. However, no study investigated or critically analyzed recent deep-learning techniques used for Arabic handwritten OCR and text correction during the period of 2020–2023. This analysis fills this noticeable gap in the literature, uncovering recent developments and their limitations for researchers, practitioners, and interested readers. The results reveal that CNN-LSTM-CTC is the most suitable architecture among Transformer and GANs for OCR because it is less complex and can hold long textual dependencies. For OCR text correction, applying DL models to generated errors in datasets improved accuracy in many works. In conclusion, Arabic OCR has the potential to further apply several text-embedding models to correct the resultant text from the OCR, and there is a significant gap in studies investigating this problem. In addition, there is a need for more high-quality and domain-specific OCR Arabic handwritten datasets. Moreover, we recommend the practical development of a space for future trends in Arabic OCR applications, derived from current limitations in Arabic OCR works and from applications in other languages; this will involve a plethora of possibilities that have not been effectively researched at the time of writing.

show abstract

mentioning

confidence: 99%