Update README.md

README.md

@@ -1,3 +1,4 @@
+
 ---
 license: other
 license_name: idl-train
@@ -17,10 +18,114 @@ size_categories:
 ### Dataset Summary
 
 Industry Documents Library (IDL) is a document dataset filtered from [UCSF documents library](https://www.industrydocuments.ucsf.edu/) with 19 million pages kept as valid samples.
-Each document exists as a collection of a pdf, a tiff image with the same contents, a json file containing extensive OCR annotation, and a .ocr file with reduced annotation. 
+Each document exists as a collection of a pdf, a tiff image with the same contents rendered, a json file containing extensive Textract OCR annotations from the [idl_data](https://github.com/furkanbiten/idl_data) project, and a .ocr file with the original, older OCR annotation.
+
+# TODO add image
+
 
 ### Usage
 
+For faster download, you can use directly the `huggingface_hub` library. Make sure `hf_transfer` is installed prior to downloading and mind that you have enough space locally. 
+
+```python
+ import os
+ 
+ os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+ 
+ from huggingface_hub import HfApi, logging
+ 
+ #logging.set_verbosity_debug()
+ hf = HfApi()
+ hf.snapshot_download("pixparse/IDL-wds", repo_type="dataset", local_dir_use_symlinks=False)
+ 
+```
+
+
+
+# TODO The following example uses fitz which is AGPL. We should also recommend the same with pypdf.
+
+```python
+from chug import create_wds_loader, create_doc_anno_pipe
+
+# TODO import image transforms and text transforms from pixparse.data
+
+
+decoder = create_decoder_pipe(
+    image_preprocess=image_preprocess, # callable of transforms to image tensors
+    anno_preprocess=anno_preprocess, # callable of transforms to text into tensors of labels and targets
+    image_key="pdf",
+    image_fmt="RGB",
+)
+
+loader = create_wds_loader(
+    "/my_data/idl-train-*.tar",
+    decoder,
+    is_train=True,
+    resampled=False,
+    start_interval=0,
+    num_samples=2159432,
+    workers=8,
+    batch_size=32, # adjust to your architecture capacity
+    seed=seed, # set a seed
+    world_size=world_size, # get world_size from your training environment
+)
+
+```
+
+Further, a metadata file `_pdfa-english-train-info-minimal.json` contains the list of samples per shard, with same basename and `.json` or `.pdf` extension, 
+as well as the count of files per shard. 
+
+
+#### Words and lines document metadata
+
+Initially, we obtained the raw data from the IDL API and combined it with the `idl_data` annotation. This information is then reshaped into lines organized in reading order, under the key lines. We keep non-reshaped word and bounding box information under the word key, should users want to use their own heuristic.
+
+The way we obtain an approximate reading order is simply by looking at the frequency peaks of the leftmost word x-coordinate. A frequency peak means that a high number of lines are starting from the same point. Then, we keep track of the x-coordinate of each such identified column. If no peaks are found, the document is assumed to be readable in plain format.
+The code to detect columns can be found here.
+```python
+def get_columnar_separators(page, min_prominence=0.3, num_bins=10, kernel_width=1):
+    """
+    Identifies the x-coordinates that best separate columns by analyzing the derivative of a histogram
+    of the 'left' values (xmin) of bounding boxes.
+
+    Args:
+        page (dict): Page data with 'bbox' containing bounding boxes of words.
+        min_prominence (float): The required prominence of peaks in the histogram.
+        num_bins (int): Number of bins to use for the histogram.
+        kernel_width (int): The width of the Gaussian kernel used for smoothing the histogram.
+
+    Returns:
+        separators (list): The x-coordinates that separate the columns, if any.
+    """
+    try:
+        left_values = [b[0] for b in page['bbox']]
+        hist, bin_edges = np.histogram(left_values, bins=num_bins)
+        hist = scipy.ndimage.gaussian_filter1d(hist, kernel_width)
+        min_val = min(hist)
+        hist = np.insert(hist, [0, len(hist)], min_val)
+        bin_width = bin_edges[1] - bin_edges[0]
+        bin_edges = np.insert(bin_edges, [0, len(bin_edges)], [bin_edges[0] - bin_width, bin_edges[-1] + bin_width])
+
+        peaks, _ = scipy.signal.find_peaks(hist, prominence=min_prominence * np.max(hist))
+        derivatives = np.diff(hist)
+
+        separators = []
+        if len(peaks) > 1:
+            # This finds the index of the maximum derivative value between peaks
+            # which indicates peaks after trough --> column
+            for i in range(len(peaks)-1):
+                peak_left = peaks[i]
+                peak_right = peaks[i+1]
+                max_deriv_index = np.argmax(derivatives[peak_left:peak_right]) + peak_left
+                separator_x = bin_edges[max_deriv_index + 1]
+                separators.append(separator_x)
+    except Exception as e:
+        separators = []
+    return separators
+```
+
+For each pdf document, we store statistics on number of pages per shard, number of valid samples per shard. A valid sample is a sample that can be encoded then decoded, which we did for each sample.
+
 This instance of IDL is in [webdataset](https://github.com/webdataset/webdataset/commits/main) .tar format. It can be used with webdataset library or current releases of Hugging Face `datasets`.
 Here is an example using the "streaming" parameter. We do recommend downloading the dataset to save bandwidth. 
 
@@ -30,8 +135,73 @@ print(next(iter(dataset['train'])).keys())
 >> dict_keys(['__key__', '__url__', 'json', 'ocr', 'pdf', 'tif'])
 ```
 
-### Data Splits
+For faster download, you can use directly the `huggingface_hub` library. Make sure `hf_transfer` is install prior to downloading and mind that you have enough space locally. 
 
+```python
+ import os
+ 
+ os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+ 
+ from huggingface_hub import HfApi, logging
+ 
+ #logging.set_verbosity_debug()
+ hf = HfApi()
+ hf.snapshot_download("pixparse/pdfa-english-train", repo_type="dataset", local_dir_use_symlinks=False)                               
+```
+### Data, metadata and statistics.
+Add example image here
+
+The metadata for each document has been formatted in this way. Each `pdf` is paired with a `json` file with the following structure. Entries have been shortened for readability.
+
+```json
+{
+  "pages": [
+    {
+      "text": [
+        "COVIDIEN",
+        "Mallinckrodt",
+        "Addendum",
+        "This Addendum to the Consulting Agreement (the \"Agreement\") of July 28, 2010 (\"Effective Date\") by",
+        "and between David Brushwod, R.Ph., J.D., with an address at P.O. Box 100496, Gainesville, FL 32610-",
+      ],
+      "bbox": [
+        [0.185964, 0.058857, 0.092199, 0.011457],
+        [0.186465, 0.079529, 0.087209, 0.009247],
+        [0.459241, 0.117854, 0.080015, 0.011332],
+        [0.117109, 0.13346, 0.751004, 0.014365],
+        [0.117527, 0.150306, 0.750509, 0.012954]
+      ],
+      "poly": [
+        [
+          {"X": 0.185964, "Y": 0.058857}, {"X": 0.278163, "Y": 0.058857}, {"X": 0.278163, "Y": 0.070315}, {"X": 0.185964, "Y": 0.070315}
+        ],
+        [
+          {"X": 0.186465, "Y": 0.079529}, {"X": 0.273673, "Y": 0.079529}, {"X": 0.273673, "Y": 0.088777}, {"X": 0.186465, "Y": 0.088777}
+        ],
+        [
+          {"X": 0.459241, "Y": 0.117854}, {"X": 0.539256, "Y": 0.117854}, {"X": 0.539256, "Y": 0.129186}, {"X": 0.459241, "Y": 0.129186}
+        ],
+        [
+          {"X": 0.117109, "Y": 0.13346}, {"X": 0.868113, "Y": 0.13346}, {"X": 0.868113, "Y": 0.147825}, {"X": 0.117109, "Y": 0.147825}
+        ],
+        [
+          {"X": 0.117527, "Y": 0.150306}, {"X": 0.868036, "Y": 0.150306}, {"X": 0.868036, "Y": 0.163261}, {"X": 0.117527, "Y": 0.163261}
+        ]
+      ],
+      "score": [
+        0.9939, 0.5704, 0.9961, 0.9898, 0.9935
+      ]
+    }
+  ]
+}
+
+```
+
+
+The top-level key, `pages`, is a list of every page in the document. The above example shows only one page. `text` is a list of lines in the document, with their individual associated bounding box in the next entry. `bbox` contains the bounding box coordinates in `left, top, width, height` format, with coordinates relative to the page size. `poly` is the corresponding polygon.
+
+`score` is the confidence score for each line obtained with Textract.
+### Data Splits
 
 #### Train
 * `idl-train-*.tar`
@@ -61,8 +231,19 @@ According to the US Copyright Office, when determining whether a particular use
 
 Each user of this website is responsible for ensuring compliance with applicable copyright laws. Persons obtaining, or later using, a copy of copyrighted material in excess of “fair use” may become liable for copyright infringement. By accessing this website, the user agrees to hold harmless the University of California, its affiliates and their directors, officers, employees and agents from all claims and expenses, including attorneys’ fees, arising out of the use of this website by the user.
 
-For more in-depth information on copyright and fair use, visit the Stanford University Libraries’ Copyright and Fair Use website.
+For more in-depth information on copyright and fair use, visit the [Stanford University Libraries’ Copyright and Fair Use website.](https://fairuse.stanford.edu/) 
 
 If you hold copyright to a document or documents in our collections and have concerns about our inclusion of this material, please see the IDL Take-Down Policy or contact us with any questions.
+
+In the dataset, the API from the Industry Documents Library holds the following permissions counts per file, showing all are now public (none are "confidential" or "privileged", only formerly.)
+
+```json
+            {'public/no restrictions': 3005133,
+             'public/formerly confidential': 264978,
+             'public/formerly privileged': 30063,
+             'public/formerly privileged/formerly confidential': 669,
+             'public/formerly confidential/formerly privileged': 397,
+             }
+```
 ### Citation Information
-??
+