Handwritten Text Dataset

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. Consequently, this dataset serves as 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, which encompasses a wide range of languages, alphabets, writing instruments, paper types, and unique handwriting styles. Consequently, it ensures a high-quality resource for OCR system development.

NumtaDB: Bengali Handwritten Digits
Handwritten Text Dataset
Handwritten Text Dataset
Handwritten Text Dataset

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.
Handwritten Text Dataset
Handwritten Text Dataset

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

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

Annotation Metrics

  • Total Transcriptions: 525,000
  • Metadata Annotations: 525,000
Handwritten Text Dataset

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.
quality dataset

Quality Data Creation

Guaranteed TAT‚Äč

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified‚Äč

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance‚Äč

HIPAA Compliance

GDPR Compliance‚Äč

GDPR Compliance

Compliance and Security‚Äč

Compliance and Security

Let's Discuss your Data collection Requirement With Us

To get a detailed estimation of requirements please reach us.

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