HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data
Enhancing degraded handwritten documents captured with smartphone cameras remains a significant challenge in document analysis. Although deep learning-based enhancement techniques have shown promise, the performance of deep learning models largely relies on the availability of meticulously labeled g...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925005761 |
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| author | K.S. Koushik Bipin Nair B J N. Shobha Rani |
| author_facet | K.S. Koushik Bipin Nair B J N. Shobha Rani |
| author_sort | K.S. Koushik |
| collection | DOAJ |
| description | Enhancing degraded handwritten documents captured with smartphone cameras remains a significant challenge in document analysis. Although deep learning-based enhancement techniques have shown promise, the performance of deep learning models largely relies on the availability of meticulously labeled ground truth datasets. To address this gap, in this study, the Handwritten Camera-Captured Dataset (HCCD) is introduced to support document enhancement and recognition tasks specific to real-world scenarios. Unlike existing datasets, which are captured in controlled environments with scanners or smartphone cameras, HCCD features real-time, camera-captured handwritten documents exhibiting a range of natural degradations. The degradation issues encompass motion blur, shadow artifacts, and uneven lighting, which reflect challenges incurred in the real-life document digitization process.In the proposed dataset, each handwritten document is paired with a high-quality enhanced image created through a combination of computer vision-based imaging techniques. The documents are in Roman script and were contributed by multiple individuals with varying handwriting styles. The dataset is valuable for machine learning/ deep learning-based training for image restoration, denoising, and OCR applications. Each sample is annotated with rich metadata for further targeted research, including degradation type, severity level, and writer-specific demographics. |
| format | Article |
| id | doaj-art-62ef11057ddd4a75bc1f3c0d76e15c3c |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-62ef11057ddd4a75bc1f3c0d76e15c3c2025-08-20T03:57:36ZengElsevierData in Brief2352-34092025-08-016111184910.1016/j.dib.2025.111849HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley DataK.S. Koushik0Bipin Nair B J1N. Shobha Rani2Department of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, IndiaDepartment of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India; Corresponding author.Department of Artificial Intelligence and Data Science, GITAM School of Technology, Bengaluru, GITAM (Deemed to be) University, IndiaEnhancing degraded handwritten documents captured with smartphone cameras remains a significant challenge in document analysis. Although deep learning-based enhancement techniques have shown promise, the performance of deep learning models largely relies on the availability of meticulously labeled ground truth datasets. To address this gap, in this study, the Handwritten Camera-Captured Dataset (HCCD) is introduced to support document enhancement and recognition tasks specific to real-world scenarios. Unlike existing datasets, which are captured in controlled environments with scanners or smartphone cameras, HCCD features real-time, camera-captured handwritten documents exhibiting a range of natural degradations. The degradation issues encompass motion blur, shadow artifacts, and uneven lighting, which reflect challenges incurred in the real-life document digitization process.In the proposed dataset, each handwritten document is paired with a high-quality enhanced image created through a combination of computer vision-based imaging techniques. The documents are in Roman script and were contributed by multiple individuals with varying handwriting styles. The dataset is valuable for machine learning/ deep learning-based training for image restoration, denoising, and OCR applications. Each sample is annotated with rich metadata for further targeted research, including degradation type, severity level, and writer-specific demographics.http://www.sciencedirect.com/science/article/pii/S2352340925005761Image restorationMobile scanningOptical character recognition (OCR)Noise reductionArtifact removal |
| spellingShingle | K.S. Koushik Bipin Nair B J N. Shobha Rani HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data Data in Brief Image restoration Mobile scanning Optical character recognition (OCR) Noise reduction Artifact removal |
| title | HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data |
| title_full | HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data |
| title_fullStr | HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data |
| title_full_unstemmed | HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data |
| title_short | HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data |
| title_sort | hccd a handwritten camera captured dataset for document enhancement under varied degradation conditionsmendeley data |
| topic | Image restoration Mobile scanning Optical character recognition (OCR) Noise reduction Artifact removal |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925005761 |
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