Update README.md

Add details on dataset

README.md

@@ -1,19 +1,114 @@
 ---
+license: cc-by-4.0
 dataset_info:
   features:
-  - name: image
-    dtype: string
-  - name: text
-    dtype: string
+    - name: image
+      dtype: string
+    - name: text
+      dtype: string
   splits:
-  - name: train
-    num_bytes: 11860671915
-    num_examples: 13320
+    - name: train
+      num_bytes: 11860671915
+      num_examples: 13320
   download_size: 11661151472
   dataset_size: 11860671915
 configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train-*
+task_categories:
+- image-to-text
+language:
+- es
+tags:
+- finance
+pretty_name: SpanishOCR
+size_categories:
+- 10K<n<100K
 ---
+---
+license: cc-by-4.0
+
+
+----------------------------------------------------------------
+# Dataset Card for SpanishOCR Dataset
+
+
+### Dataset Summary
+The SpanishOCR dataset contains images derived from regulatory documents from Peru government in pdf format. This dataset is used for benchmarkingg and evaluating Large Language Models ability on converting unstructured dcuments, such as pdfs and images, into machine readable format, particularly in finance domain, where the conversion task is more complex and valuable.
+
+### Supported Tasks
+
+- **Task:** Information Extraction
+- **Evaluation Metrics:** ROUGE-1
+
+### Languages
+
+- Spanish
+
+## Dataset Structure
+
+### Data Instances
+Each instance in the SpanishOCR dataset comprises 2 fields:
+- **image** : image of regulatory document, each image represent one page in pdf
+- **text**: ground truth of tet extracted from regulatory document
+
+### Data Fields
+- **image** : string - Base64-encoded png
+- **text**: extracted text from pdf files
+
+## Dataset Creation
+
+### Curation Rationale
+The SpanishOCR dataset was curated to support research and development on information extraction techniques and layout retain ability for unstructured documents in Spanish. By providing real-world regulatory documents in unstructured format with ground truth, the dataset seeks to address challenges in extracting informat as well as layouts and convert into machine-readable format.
+### Source Data
+
+#### Initial Data Collection and Normalization
+- The source data are regulatory documents for Securities Market from Peru government publically available.
+- The pdf files of those documents are downloaded and split via API, split into page per file, and convert into images. 
+
+#### Who are the Source Language Producers?
+- The source data are regulatory documents for Securities Market from Peru government, and is collected to a Peruvian digital platform: https://www.gob.pe/institucion/smv/buscador?contenido=normas&sheet=1&sort_by=re
+
+### Annotations
+
+#### Annotation Process
+- The dataset was prepared by collecting, spliting, and converting regulatory documents in Spanish
+- The annotation of ground truth text is done by Python OCR package ```fitz```
+
+#### Who are the Annotators?
+
+- The dataset stems from publicly available regulatory documents.
+- No external annotation team was involved beyond this.
+
+### Personal and Sensitive Information
+
+- The SpanishOCR dataset does not contain any personally identifiable information (PII) and is strictly focused on Greek text data for summarization purposes.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+This dataset enables AI models to extract structured information from scanned financial documents in multiple languages, promoting transparency and accessibility. By aligning page-level PDF images with accurate ground truth text, it supports the development of fairer, more inclusive models that work across diverse formats and languages. 
+
+### Discussion of Biases
+- The source data is limited toregulatory documents for Scurity Markets, it may underrepresent other financial document types such as tax records, bank statements, or private company reports, potentially limiting model generalizability.
+
+### Other Known Limitations
+- The ground truth text is extracted using the Python package fitz (PyMuPDF), which may introduce inaccuracies in complex layouts, potentially affecting training quality and evaluation reliability.
+- While the dataset covers regulatory documents, it may lack sufficient variety in layout styles (e.g., handwritten notes, non-standard financial forms, embedded charts), which could limit a model’s ability to generalize to less structured or unconventional financial documents.
+
+## Additional Information
+
+### Dataset Curators
+- Yueru He
+- Ruoyu Xiang
+
+### Licensing Information
+
+- **License:** CC BY 4.0
+
+### Citation Information
+
+If you use this dataset in your research, please consider citing the following paper:
+