| | --- |
| | license: mit |
| | language: |
| | - en |
| | task_categories: |
| | - image-to-text |
| | pretty_name: IAM-line |
| |
|
| | dataset_info: |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: text |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_examples: 6482 |
| | - name: validation |
| | num_examples: 976 |
| | - name: test |
| | num_examples: 2915 |
| | dataset_size: 10373 |
| | tags: |
| | - atr |
| | - htr |
| | - ocr |
| | - modern |
| | - handwritten |
| | --- |
| | |
| | # IAM - line level |
| |
|
| | ## Table of Contents |
| | - [IAM - line level](#iam-line-level) |
| | - [Table of Contents](#table-of-contents) |
| | - [Dataset Description](#dataset-description) |
| | - [Dataset Summary](#dataset-summary) |
| | - [Languages](#languages) |
| | - [Dataset Structure](#dataset-structure) |
| | - [Data Instances](#data-instances) |
| | - [Data Fields](#data-fields) |
| |
|
| | ## Dataset Description |
| |
|
| | - **Homepage:** [IAM Handwriting Database](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database) |
| | - **Paper:** [The IAM-database: an English sentence database for offline handwriting recognition](https://doi.org/10.1007/s100320200071) |
| | - **Point of Contact:** [TEKLIA](https://teklia.com) |
| |
|
| | ## Dataset Summary |
| |
|
| | 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. |
| |
|
| | Note that all images are resized to a fixed height of 128 pixels. |
| |
|
| | ### Languages |
| |
|
| | All the documents in the dataset are written in English. |
| |
|
| | ## Dataset Structure |
| |
|
| | ### Data Instances |
| |
|
| | ``` |
| | { |
| | 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2467x128 at 0x1A800E8E190, |
| | 'text': 'put down a resolution on the subject' |
| | } |
| | ``` |
| |
|
| | ### Data Fields |
| |
|
| |
|
| | - `image`: a PIL.Image.Image object containing the image. Note that when accessing the image column (using 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]. |
| | - `text`: the label transcription of the image. |
| |
|
