Datasets:
yschneider
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license: mit
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---
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---
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license: mit
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language:
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- en
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task_categories:
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- image-to-text
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pretty_name: IAM
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dataset_info:
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features:
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- name: image
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dtype: image
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- name: text
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dtype: string
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splits:
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- name: train
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num_examples: 6481
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- name: validation
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num_examples: 976
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- name: test
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num_examples: 2914
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dataset_size: 10373
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---
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# Esposalles Dataset
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## Table of Contents
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- [Esposalles Dataset](#esposalles-dataset)
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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## Dataset Description
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- **Homepage:** [IAM](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database)
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- **Paper:** [Paper](https://doi.org/10.1007/s100320200071)
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- **Point of Contact:** [TEKLIA](https://teklia.com)
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## Dataset Summary
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The IAM Handwriting Database contains forms of handwritten English text which can be used to train and test handwritten text recognizers and to perform writer identification and verification experiments.
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### Languages
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All the documents in the dataset are written in English.
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## Dataset Structure
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### Data Instances
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```
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{
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'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2467x128 at 0x1A800E8E190,
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'text': 'put down a resolution on the subject'
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}
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```
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### Data Fields
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- `image`: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
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- `text`: the label transcription of the image.
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