Update README.md

README.md

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-# Dataset Card for Suzhou Numeral Dataset
-![Sample Image](assets/KM_48690-0002-u.jpg)
-This dataset contains annotated images of Suzhou numerals (0-9) extracted from historical archival records of the Hechang Firm in Nagasaki, documenting trade activities between China, Japan, and Southeast Asia from 1880 to 1930. It is designed to support research in historical document analysis, optical character recognition (OCR), and the study of traditional numeral systems.
+# Dataset Card for Suzhou Numeral Dataset 🌟
+![Sample Image](assets/KM_48690-0002-u.jpg)  
+This dataset contains annotated images of Suzhou numerals (0-9) extracted from historical archival records of the Hechang Firm in Nagasaki 📜, documenting trade activities between China, Japan, and Southeast Asia from 1880 to 1930. It’s designed to support research in historical document analysis, optical character recognition (OCR) 🔍, and the study of traditional numeral systems 📊.
 
-## Dataset Details
+## Dataset Details 🎉
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
+<!-- Provide a longer summary of what this dataset is. -->  
+This dataset comprises **773 annotated instances** of Suzhou numerals derived from the Hechang Firm in Nagasaki archive, a collection of accounting ledgers, trade contracts, and commercial correspondence 💼. The dataset includes high-resolution .png images of Suzhou numerals alongside their corresponding labels in CSV files, capturing natural variations in notation style, stroke thickness, and alignment ✍️. It is split into **training (541 samples)**, **testing (116 samples)**, and **validation (116 samples)** sets with a 7:1.5:1.5 ratio.  
+![Sample Image](assets/flow_chart_v8.jpg)  
 
-This dataset comprises 773 annotated instances of Suzhou numerals derived from the Hechang Firm in Nagasaki archive, a collection of accounting ledgers, trade contracts, and commercial correspondence. The dataset includes high-resolution .png images of Suzhou numerals alongside their corresponding labels in CSV files, capturing natural variations in notation style, stroke thickness, and alignment. It is split into training (541 samples), testing (116 samples), and validation (116 samples) sets with a 7:1.5:1.5 ratio.
-![Sample Image](assets/flow_chart_v8.jpg)
+- **Curated by:** [More Information Needed]  
+- **Funded by [optional]:** [More Information Needed]  
+- **Shared by [optional]:** [More Information Needed]  
+- **Language(s) (NLP):** [More Information Needed]  
+- **License:** [More Information Needed]  
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
+### Dataset Sources [optional] 🔗
 
-### Dataset Sources [optional]
+<!-- Provide the basic links for the dataset. -->  
+- **Repository:** [More Information Needed]  
+- **Paper [optional]:** [More Information Needed]  
+- **Demo [optional]:** [More Information Needed]  
 
-<!-- Provide the basic links for the dataset. -->
+## Uses 🚀
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+<!-- Address questions around how the dataset is intended to be used. -->  
 
-## Uses
+### Direct Use ✅  
+This dataset is suitable for:  
+- Training OCR models to recognize Suzhou numerals 🤖.  
+- Studying historical variations in numeral notation and document formatting 📚.  
+- Benchmarking image classification algorithms on real-world handwritten data 🧪.  
+[More Information Needed]  
 
-<!-- Address questions around how the dataset is intended to be used. -->
+### Out-of-Scope Use 🚫  
+The dataset is **not intended** for:  
+- General-purpose digit recognition beyond Suzhou numerals.  
+- Applications requiring modern numeral systems or non-historical contexts.  
+[More Information Needed]  
 
-### Direct Use
+## Dataset Structure 🗂️
 
-<!-- This section describes suitable use cases for the dataset. -->
-This dataset is suitable for:
-
-- Training OCR models to recognize Suzhou numerals.
-- Studying historical variations in numeral notation and document formatting.
-- Benchmarking image classification algorithms on real-world handwritten data.
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-The dataset is not intended for:
-
-- General-purpose digit recognition beyond Suzhou numerals.
-- Applications requiring modern numeral systems or non-historical contexts.
-[More Information Needed]
-
-## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->  
 ```
 The dataset is organized as follows:
 ├── train.csv          # Training set metadata (541 samples)
@@ -80,79 +72,64 @@ The dataset is organized as follows:
 The splits were created with a 7:1.5:1.5 ratio to ensure a balanced distribution for training, testing, and validation.
 [More Information Needed]
 
-## Dataset Creation
+## Dataset Creation ✨
 
-### Curation Rationale
+### Curation Rationale  
+<!-- Motivation for the creation of this dataset. -->  
+This dataset was created to **preserve and analyze** the use of Suzhou numerals in historical trade records, enabling research into traditional numeral systems and supporting the development of OCR tools for historical document digitization 🛠️.  
+[More Information Needed]  
 
-<!-- Motivation for the creation of this dataset. -->
-This dataset was created to preserve and analyze the use of Suzhou numerals in historical trade records, enabling research into traditional numeral systems and supporting the development of OCR tools for historical document digitization.
-[More Information Needed]
+### Source Data  
 
-### Source Data
+#### Data Collection and Processing  
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->  
+The dataset originates from the Hechang Firm in Nagasaki archive, spanning **1880–1930**. The process involved:  
+1. Scanning documents into high-resolution PDFs 📑.  
+2. Manually identifying and annotating Suzhou numerals (0-9) within transaction records, cost lists, and handwritten text ✍️.  
+3. Cropping annotated sections into individual .png images, each paired with a label 🖼️.  
+4. Cross-verifying ambiguous cases (e.g., faded or overlapping strokes) by multiple annotators for consistency ✅.  
+[More Information Needed]  
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+#### Who are the source data producers?  
+The source data was produced by **Chinese merchants and traders** associated with the Hechang Firm in Nagasaki, who used Suzhou numerals in their accounting and correspondence 💰. No specific demographic details are available.  
+[More Information Needed]  
 
-#### Data Collection and Processing
+### Annotations [optional]  
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->  
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-The dataset originates from the Hechang Firm in Nagasaki archive, spanning 1880–1930. The process involved:
+#### Annotation process  
+Human experts manually annotated Suzhou numerals in the digitized documents. The process included:  
+- Identifying numeral instances in PDFs 🔍.  
+- Assigning labels based on the depicted numeral ✍️.  
+- Cropping images and validating annotations, with multiple annotators resolving ambiguities ✅.  
+[More Information Needed]  
 
-1. Scanning documents into high-resolution PDFs.
-2. Manually identifying and annotating Suzhou numerals (0-9) within transaction records, cost lists, and handwritten text.
-3. Cropping annotated sections into individual .png images, each paired with a label.
-4. Cross-verifying ambiguous cases (e.g., faded or overlapping strokes) by multiple annotators for consistency.
-[More Information Needed]
+#### Who are the annotators?  
+The annotators were **human experts** familiar with historical Chinese numerals and document analysis 🧑‍🏫.  
+[More Information Needed]  
 
-#### Who are the source data producers?
+#### Personal and Sensitive Information  
+The dataset contains **no personal or sensitive information**, as it focuses solely on numeral images extracted from historical trade records. No identifiable data (e.g., names, addresses) is included 🚫.  
+[More Information Needed]  
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-The source data was produced by Chinese merchants and traders associated with the Hechang Firm in Nagasaki, who used Suzhou numerals in their accounting and correspondence. No specific demographic details are available.
-[More Information Needed]
+## Bias, Risks, and Limitations ⚠️
 
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->  
+- **Bias**: The dataset reflects Suzhou numeral usage specific to the Hechang Firm’s records (1880–1930), which may not generalize to other regions or time periods 🌍.  
+- **Risks**: Misinterpretation of faded or ambiguous numerals could affect model performance 😕.  
+- **Limitations**: Limited sample size (773 instances) and focus on a niche numeral system may restrict broader applicability 📉.  
+[More Information Needed]  
 
-#### Annotation process
+### Recommendations 💡  
+Users should be aware of the **historical and regional specificity** of the dataset. For robust OCR applications, supplementing with additional Suzhou numeral data from other sources is recommended 🌟.  
+Users should be made aware of the risks, biases, and limitations of the dataset. More information needed for further recommendations.  
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-Human experts manually annotated Suzhou numerals in the digitized documents. The process included:
+## Citation [optional] 📖
 
-- Identifying numeral instances in PDFs.
-- Assigning labels based on the depicted numeral.
-- Cropping images and validating annotations, with multiple annotators resolving ambiguities.
-[More Information Needed]
-
-#### Who are the annotators?
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->  
 
-<!-- This section describes the people or systems who created the annotations. -->
-The annotators were human experts familiar with historical Chinese numerals and document analysis.
-[More Information Needed]
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-The dataset contains no personal or sensitive information, as it focuses solely on numeral images extracted from historical trade records. No identifiable data (e.g., names, addresses) is included.
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-- **Bias**: The dataset reflects Suzhou numeral usage specific to the Hechang Firm’s records (1880–1930), which may not generalize to other regions or time periods.
-- **Risks**: Misinterpretation of faded or ambiguous numerals could affect model performance.
-- **Limitations:** Limited sample size (773 instances) and focus on a niche numeral system may restrict broader applicability.
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be aware of the historical and regional specificity of the dataset. For robust OCR applications, supplementing with additional Suzhou numeral data from other sources is recommended.
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
+**BibTeX:**  
+```bibtex
 @misc{suzhou_numeral_dataset,
   title = {Suzhou Numeral Dataset},
   author = {[More Information Needed]},
@@ -160,7 +137,7 @@ Users should be made aware of the risks, biases and limitations of the dataset.
   publisher = {Hugging Face},
   url = {[More Information Needed]}
 }
-[More Information Needed]
+```
 
 **APA:**
 [More Information Needed]. (2025). Suzhou Numeral Dataset. Hugging Face. [More Information Needed]