Update README.md

Author: balaboom123

README.md

@@ -1,10 +1,20 @@
-# ASL Translation Data Preprocessing
+# ASL Translation Data Preprocessing<!-- omit from toc -->
 
-This repository follows the methodology described in the YouTube-ASL Dataset paper and provides a comprehensive solution for preprocessing American Sign Language (ASL) datasets, specifically designed to handle both **How2Sign** and **YouTube-ASL** datasets. Our preprocessing pipeline streamlines the workflow from video acquisition to landmark extraction, making the data ready for ASL translation tasks.
+This repository provides a comprehensive solution for preprocessing American Sign Language (ASL) datasets, designed to handle both **How2Sign** and **YouTube-ASL** datasets. Our pipeline streamlines the workflow from video acquisition to landmark extraction, preparing the data for ASL translation tasks.
+
+## Table of Contents<!-- omit from toc -->
+
+- [Project Configuration](#project-configuration)
+- [How to Use](#how-to-use)
+  - [YouTube-ASL](#youtube-asl)
+  - [How2Sign](#how2sign)
+- [Dataset Introduction](#dataset-introduction)
+  - [YouTube-ASL Dataset](#youtube-asl-dataset)
+  - [How2Sign Dataset](#how2sign-dataset)
 
 ## Project Configuration
 
-All project settings are centrally managed through `conf.py`, providing a single point of configuration for the entire preprocessing pipeline. Key configuration elements include:
+All project settings are managed through `conf.py`, offering a single configuration point for the preprocessing pipeline. Key elements include:
 
 - `ID`: Text file containing YouTube video IDs to process
 - `VIDEO_DIR`: Directory for downloaded videos
@@ -17,38 +27,39 @@ All project settings are centrally managed through `conf.py`, providing a single
 - `FRAME_SKIP`: Controls frame sampling rate for efficient processing
 - `MAX_WORKERS`: Manages parallel processing to optimize performance
 
-- `POSE_IDX`, `FACE_IDX`, `HAND_IDX`: Selected landmark indices for extracting the most relevant points for sign language analysis
-
-This centralized approach allows easy adaptation to different hardware capabilities or dataset requirements without modifying the core processing code.
-## How To Use?
-- **YouTube-ASL**: make sure the constant is correct in conf.py. Then, operate step 1 to step 3. 
-- **How2Sign**: download **Green Screen RGB videos** and **English Translation (manually re-aligned)** from How2Sign website. Put the directory and .csv file in the right path or amend the path in the conf.py. then, operate step 3 only.
-  
-### Step 1: Data Acquisition (s1_data_downloader.py)
-**Necessary Constants:**`ID`, `VIDEO_DIR`, `TRANSCRIPT_DIR`, `YT_CONFIG`, `LANGUAGE`  
-The script intelligently skips already downloaded content and implements rate limiting to prevent API throttling.
+- `POSE_IDX`, `FACE_IDX`, `HAND_IDX`: Selected landmark indices for extracting relevant points for sign language analysis
 
+## How to Use
 
+### YouTube-ASL
+1. Ensure the constants in `conf.py` are correct.
+2. Run the following steps in order:
+   - **Step 1: Data Acquisition** (`s1_data_downloader.py`)
+     - **Necessary Constants:** `ID`, `VIDEO_DIR`, `TRANSCRIPT_DIR`, `YT_CONFIG`, `LANGUAGE`
+     - The script skips already downloaded content and implements rate limiting to prevent API throttling.
 
-### Step 2: Transcript Processing (s2_transcript_preprocess.py)
-**Necessary Constants:** `ID`, `TRANSCRIPT_DIR`, `CSV_FILE`  
-This step cleans text (converts Unicode characters, removes brackets), filters segments based on length and duration, and saves them with precise timestamps as tab-separated values.
+   - **Step 2: Transcript Processing** (`s2_transcript_preprocess.py`)
+     - **Necessary Constants:** `ID`, `TRANSCRIPT_DIR`, `CSV_FILE`
+     - This step cleans text (converts Unicode characters, removes brackets), filters segments based on length and duration, and saves them with precise timestamps as tab-separated values.
 
+   - **Step 3: Feature Extraction** (`s3_mediapipe_labelling.py`)
+     - **Necessary Constants:** `CSV_FILE`, `VIDEO_DIR`, `OUTPUT_DIR`, `MAX_WORKERS`, `FRAME_SKIP`, `POSE_IDX`, `FACE_IDX`, `HAND_IDX`
+- The script processes each video segment according to its timestamp, extracting only the most relevant body keypoints for sign language analysis. It uses parallel processing to handle multiple video efficiently. Results are saved as NumPy arrays.
 
-
-### Step 3: Feature Extraction (s3_mediapipe_labelling.py)
-**Necessary Constants:** `CSV_FILE`, `VIDEO_DIR`, `OUTPUT_DIR`, `MAX_WORKERS`, `FRAME_SKIP`, `POSE_IDX`, `FACE_IDX`, `HAND_IDX`  
-The script processes each video segment according to its timestamp, extracting only the most relevant body keypoints for sign language analysis. Results are saved as NumPy arrays.
+### How2Sign
+1. Download **Green Screen RGB videos** and **English Translation (manually re-aligned)** from the How2Sign website.
+2. Place the directory and .csv file in the correct path or amend the path in `conf.py`.
+3. Run **Step 3: Feature Extraction** (`s3_mediapipe_labelling.py`) only.
 
 ## Dataset Introduction
 
 ### YouTube-ASL Dataset
-Video List: [https://github.com/google-research/google-research/blob/master/youtube_asl/README.md](https://github.com/google-research/google-research/blob/master/youtube_asl/README.md)  
-Paper: ["YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus" (Uthus et al., 2023)](https://arxiv.org/abs/2306.15162).
+- **Video List**: [GitHub Repository](https://github.com/google-research/google-research/blob/master/youtube_asl/README.md)
+- **Paper**: ["YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus" (Uthus et al., 2023)](https://arxiv.org/abs/2306.15162)
 
 If you use YouTube-ASL, please cite their associated paper:
 
-```
+```bibtex
 @misc{uthus2023youtubeasl,
   author = {Uthus, David and Tanzer, Garrett and Georg, Manfred},
   title = {YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus},
@@ -60,12 +71,12 @@ If you use YouTube-ASL, please cite their associated paper:
 ```
 
 ### How2Sign Dataset
-Dataset: [https://how2sign.github.io/](https://how2sign.github.io/)  
-Paper: [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://openaccess.thecvf.com/content/CVPR2021/html/Duarte_How2Sign_A_Large-Scale_Multimodal_Dataset_for_Continuous_American_Sign_Language_CVPR_2021_paper.html)
+- **Dataset**: [How2Sign Website](https://how2sign.github.io/)
+- **Paper**: [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://openaccess.thecvf.com/content/CVPR2021/html/Duarte_How2Sign_A_Large-Scale_Multimodal_Dataset_for_Continuous_American_Sign_Language_CVPR_2021_paper.html)
 
 If you use How2Sign, please cite their associated paper:
 
-```
+```bibtex
 @inproceedings{Duarte_CVPR2021,
     title={{How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language}},
     author={Duarte, Amanda and Palaskar, Shruti and Ventura, Lucas and Ghadiyaram, Deepti and DeHaan, Kenneth and