Handwritten Text Dataset

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Project Overview:

Objective

We specialize in assembling datasets to train cutting-edge machine learning models. For instance, in our recent project, we aimed to enhance Optical Character Recognition (OCR) systems by creating an extensive dataset of handwritten texts. As a result, we have developed a crucial tool for improving OCR accuracy in interpreting various handwriting styles.

Scope

Our team has successfully compiled a rich collection of handwritten documents. We meticulously transcribed and annotated this dataset, encompassing a wide range of languages, alphabets, writing instruments, paper types, and unique handwriting styles. As a result, we have ensured a high-quality resource for OCR system development.

Sources

Personal letters and postcards that have been carefully collected and successfully curated.
Moreover, handwritten essays and assignments have been meticulously gathered and thoughtfully curated.
Additionally, grocery lists and notes have been successfully collected and organized.
Furthermore, business forms and applications have been carefully collected and successfully curated.
In addition, diaries and journals have been thoughtfully collected and successfully curated for comprehensive insights.
Lastly, random scribbles and quick notes have been meticulously gathered and successfully curated.

Data Collection Metrics

Total Handwritten Entries: 525,000
Letters/Postcards: 105,000
Essays/Assignments: 157,500
Lists/Notes: 105,000
Business Forms: 52,500
Diaries/Journals: 73,500
Scribbles: 31,500

Annotation Process

Stages

Image Pre-processing: Firstly, we refined each image for optimal clarity.
Text Transcription: Secondly, our team manually transcribed the handwritten content.
Metadata Annotation: Moreover, we tagged each entry with metadata such as language and writing tool.
Validation: Finally, our rigorous validation process involved both peer review and automated OCR tools.

Annotation Metrics

Total Transcriptions: 525,000
Metadata Annotations: 525,000

Quality Assurance

Stages

Automated OCR Verification
Peer Review
Inter-annotator Agreement.

QA Metrics

Annotations Verified with OCR: 262,500
Transcriptions Peer Reviewed: 367,500
Inconsistencies Identified and Rectified: 15,750

Conclusion

We have assembled a diverse and comprehensive Handwritten Text Dataset, setting a new standard for OCR system training. Our meticulous data collection and annotation process supports academic research and enhances practical OCR applications. This showcases our commitment to advancing machine learning technologies.

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