Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- ar
+task_categories:
+- image-to-text
+pretty_name: KHATT_v1.0
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 4672
+  - name: validation
+    num_examples: 963
+  - name: test
+    num_examples: 1038
+  dataset_size: 220M
+tags:
+- atr
+- htr
+- ocr
+- historical
+- handwritten
+- arabic
+---
+
+# Samaritan v1 - line level
+
+## Table of Contents
+- [KHATT_v1.0 - line level](#KHATT_v1.0_dataset)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+
+## Dataset Description
+
+- **Homepage:** [johnlockejrr's personal project](https://huggingface.co/datasets/johnlockejrr/KHATT_v1.0_dataset)
+
+## Dataset Summary 
+
+KHATT (KFUPM Handwritten Arabic TexT) database is a database of unconstrained handwritten Arabic Text written by 1000 different writers. This research database’s development was undertaken by a research group from KFUPM, Dhahran, S audi Arabia headed by Professor Sabri Mahmoud in collaboration with Professor Fink from TU-Dortmund, Germany and Dr. Märgner from TU-Braunschweig, Germany.
+
+The database includes 2000 similar-text paragraph images and 2000 unique-text paragraph images and their extracted text line images. The images are accompanied with manually verified ground-truth and Latin representation of the ground-truth. The database can be used in various handwriting recognition related researches like, but not limited to, text recognition, and writer identification. Interested readers can refer to the paper [1], and [2] for more details on the database. The version 1.0 of the KHATT database is available free of charge (for academic and research purposes) to the researchers.
+
+Database Overview:
+
+- Forms written by 1000 different writers.
+- Scanned at different resolutions (200, 300, and 600 DPIs).
+- Writers are from different countries, gender, age groups, handedness and education level.
+- Natural writings with unrestricted writing styles.
+- 2000 unique paragraph images and their segmented line images (source text from different topics like arts, education, health, nature, technology).
+- 2000 paragraph images containing similar text, each covering all Arabic characters and shapes and their segmented line images.
+- Free paragraphs written by writers on any topic of their choice.
+- Paragraph and line images are supplied with manually verified ground-truths.
+- The database divided into three disjoint sets viz. training (70%), validation (15%), and testing (15%).
+- Promote research in areas like writer identification, line segmentation, and binarization and noise removal techniques beside handwritten text recognition.
+
+### Languages
+
+All the documents in the dataset are written in Arabic.
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4300x128 at 0x1A800E8E190,
+  'text': 'رفاظ قيار يؤل نب فوؤر هبحصب ماغرض رفظم حون بهذ'
+}
+```
+
+### 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. The text was intentionally flipped from RTL to LTR because of PyLaia library limitation to LTR.