First commit - not yet sharing the data

README.md

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 ---
-license: cc-by-nc-sa-4.0
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|iit_cdip
+task_categories:
+- image-classification
+task_ids:
+- multi-class-image-classification
+paperswithcode_id: rvl-cdip
+pretty_name: RVL-CDIP
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': letter
+          '1': form
+          '2': email
+          '3': handwritten
+          '4': advertisement
+          '5': scientific report
+          '6': scientific publication
+          '7': specification
+          '8': file folder
+          '9': news article
+          '10': budget
+          '11': invoice
+          '12': presentation
+          '13': questionnaire
+          '14': resume
+          '15': memo
+  splits:
+  - name: train
+    num_bytes: 38816373360
+    num_examples: 320000
+  - name: test
+    num_bytes: 4863300853
+    num_examples: 40000
+  - name: validation
+    num_bytes: 4868685208
+    num_examples: 40000
+  download_size: 38779484559
+  dataset_size: 48548359421
 ---
+
+# Dataset Card for RVL-CDIP
+
+## Extension
+
+The data loader provides support for loading easyOCR files together with the images
+It is not included under '../data', yet is available upon request via email <firstname@contract.fit>.
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [The RVL-CDIP Dataset](https://www.cs.cmu.edu/~aharley/rvl-cdip/)
+- **Repository:**
+- **Paper:** [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://arxiv.org/abs/1502.07058)
+- **Leaderboard:** [RVL-CDIP leaderboard](https://paperswithcode.com/dataset/rvl-cdip)
+- **Point of Contact:** [Adam W. Harley](mailto:aharley@cmu.edu)
+
+### Dataset Summary
+
+The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.
+
+### Supported Tasks and Leaderboards
+
+- `image-classification`: The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available [here](https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip).
+
+### Languages
+
+All the classes and documents use English as their primary language.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from the training set is provided below :
+```
+{
+    'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=754x1000 at 0x7F9A5E92CA90>,
+    'label': 15
+}
+```
+
+### Data Fields
+
+- `image`: A `PIL.Image.Image` object containing a document.
+- `label`: an `int` classification label.
+
+<details>
+  <summary>Class Label Mappings</summary>
+
+```json
+{
+  "0": "letter",
+  "1": "form",
+  "2": "email",
+  "3": "handwritten",
+  "4": "advertisement",
+  "5": "scientific report",
+  "6": "scientific publication",
+  "7": "specification",
+  "8": "file folder",
+  "9": "news article",
+  "10": "budget",
+  "11": "invoice",
+  "12": "presentation",
+  "13": "questionnaire",
+  "14": "resume",
+  "15": "memo"
+}
+```
+
+</details>
+
+### Data Splits
+
+|   |train|test|validation|
+|----------|----:|----:|---------:|
+|# of examples|320000|40000|40000|
+
+The dataset was split in proportions similar to those of ImageNet.
+- 320000 images were used for training,
+- 40000 images for validation, and 
+- 40000 images for testing. 
+
+## Dataset Creation
+
+### Curation Rationale
+
+From the paper:
+> This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000
+document images across 16 categories, useful for training new CNNs for document analysis.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The same as in the IIT-CDIP collection.
+
+#### Who are the source language producers?
+
+The same as in the IIT-CDIP collection.
+
+### Annotations
+
+#### Annotation process
+
+The same as in the IIT-CDIP collection.
+
+#### Who are the annotators?
+
+The same as in the IIT-CDIP collection.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis.
+
+### Licensing Information
+
+RVL-CDIP is a subset of IIT-CDIP, which came from the [Legacy Tobacco Document Library](https://www.industrydocuments.ucsf.edu/tobacco/), for which license information can be found [here](https://www.industrydocuments.ucsf.edu/help/copyright/).
+
+### Citation Information
+
+```bibtex
+@inproceedings{harley2015icdar,
+    title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
+    author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
+    booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
+    year = {2015}
+}
+```
+
+### Contributions
+
+Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset.

data/rvl-cdip.tar.gz

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dataset_infos.json

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+{"default": {"description": "The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images.\n", "citation": "@inproceedings{harley2015icdar,\n    title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},\n    author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},\n    booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},\n    year = {2015}\n}\n", "homepage": "https://www.cs.cmu.edu/~aharley/rvl-cdip/", "license": "https://www.industrydocuments.ucsf.edu/help/copyright/", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "label": {"num_classes": 16, "names": ["letter", "form", "email", "handwritten", "advertisement", "scientific report", "scientific publication", "specification", "file folder", "news article", "budget", "invoice", "presentation", "questionnaire", "resume", "memo"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "image", "output": "label"}, "task_templates": [{"task": "image-classification", "image_column": "image", "label_column": "label"}], "builder_name": "rvl_cdip", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 38816373360, "num_examples": 320000, "dataset_name": "rvl_cdip"}, "test": {"name": "test", "num_bytes": 4863300853, "num_examples": 40000, "dataset_name": "rvl_cdip"}, "validation": {"name": "validation", "num_bytes": 4868685208, "num_examples": 40000, "dataset_name": "rvl_cdip"}}, "download_checksums": {"https://huggingface.co/datasets/rvl_cdip/resolve/main/data/rvl-cdip.tar.gz": {"num_bytes": 38762320458, "checksum": "3577e655813922098cd776422479017be37612ec17a65076b1b62199bf8b28a2"}, "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/train.txt": {"num_bytes": 13730846, "checksum": "8d25bf09a99d8691883dfebbc783046fb963437ce90b313ad0b81cab451fc17b"}, "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/test.txt": {"num_bytes": 1717144, "checksum": "97699c1c56425c4482632742381289b7bf855c23cd020253d7cb29df638ba1a3"}, "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/val.txt": {"num_bytes": 1716111, "checksum": "748483211bda619ab5fc3d395bd7dbfb6cac025753a13fc6abe500280e4e963a"}}, "download_size": 38779484559, "post_processing_size": null, "dataset_size": 48548359421, "size_in_bytes": 87327843980}}

rvl_cdip_easyOCR.py

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+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset"""
+
+
+import os
+import numpy as np
+from tqdm import tqdm
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_CITATION = """\
+@inproceedings{harley2015icdar,
+    title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
+    author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
+    booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
+    year = {2015}
+}
+"""
+
+
+_DESCRIPTION = """\
+The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images.
+"""
+
+
+_HOMEPAGE = "https://www.cs.cmu.edu/~aharley/rvl-cdip/"
+
+
+_LICENSE = "https://www.industrydocuments.ucsf.edu/help/copyright/"
+
+
+_URLS = {
+    "rvl-cdip": "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/rvl-cdip.tar.gz",
+}
+
+_METADATA_URLS = {
+    "train": "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/train.txt",
+    "test": "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/test.txt",
+    "val": "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/val.txt",
+}
+
+_CLASSES = [
+    "letter",
+    "form",
+    "email",
+    "handwritten",
+    "advertisement",
+    "scientific report",
+    "scientific publication",
+    "specification",
+    "file folder",
+    "news article",
+    "budget",
+    "invoice",
+    "presentation",
+    "questionnaire",
+    "resume",
+    "memo",
+]
+
+_IMAGES_DIR = "images/"
+
+
+class RvlCdip(datasets.GeneratorBasedBuilder):
+    """Ryerson Vision Lab Complex Document Information Processing dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=_CLASSES),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=[
+                ImageClassification(image_column="image", label_column="label")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.data_files:
+            archive_path = self.config.data_files["binary"][0]
+        else:
+            archive_path = dl_manager.download(
+                _URLS["rvl-cdip"]
+            )  # only download images if need be
+        labels_path = dl_manager.download(_METADATA_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "archive_iterator": dl_manager.iter_archive(archive_path),
+                    "labels_filepath": labels_path["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "archive_iterator": dl_manager.iter_archive(archive_path),
+                    "labels_filepath": labels_path["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "archive_iterator": dl_manager.iter_archive(archive_path),
+                    "labels_filepath": labels_path["val"],
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    @staticmethod
+    def _get_image_to_class_map(data):
+        image_to_class_id = {}
+        for item in data:
+            image_path, class_id = item.split(" ")
+            image_path = os.path.join(_IMAGES_DIR, image_path)
+            image_to_class_id[image_path] = int(class_id)
+
+        return image_to_class_id
+
+    @staticmethod
+    def _get_image_to_OCR(OCR_dir, split):
+        def parse_easyOCR_box(box):
+            # {'x0': 39, 'y0': 39, 'x1': 498, 'y1': 82, 'width': 459, 'height': 43}
+            return (box["x0"], box["y0"], box["x1"], box["y1"])
+
+        if OCR_dir is None:
+            return {}
+        image_to_OCR = {}
+        data = np.load(
+            os.path.join(OCR_dir, f"Easy_{split[0].upper()+split[1:]}_Data.npy"),
+            allow_pickle=True,
+        )
+        for ex in tqdm(data, desc='Loading OCR data'):
+            w, h = ex["images"][0]["image_width"], ex["images"][0]["image_height"]
+            filename = ex["images"][0]["file_name"]
+            words = ex["word-level annotations"][0]["ocred_text"]
+            box_info = ex["word-level annotations"][0]["ocred_boxes"]
+            boxes = [parse_easyOCR_box(box) for box in box_info]
+            assert len(boxes) == len(words)
+            image_to_OCR[filename] = (words, boxes)
+        return image_to_OCR
+
+    @staticmethod
+    def _path_to_OCR(image_to_OCR, file_path):
+        # obtain text and boxes given file_path
+        text, boxes = None, None
+        if file_path in image_to_OCR:
+            text, boxes = image_to_OCR[file_path]
+        return text, boxes
+
+    def _generate_examples(self, archive_iterator, labels_filepath, split):
+        with open(labels_filepath, encoding="utf-8") as f:
+            data = f.read().splitlines()
+
+        image_to_OCR = self._get_image_to_OCR(self.config.data_dir, split)
+        image_to_class_id = self._get_image_to_class_map(data)
+
+        for file_path, file_obj in archive_iterator:
+            if file_path.startswith(_IMAGES_DIR):
+                if file_path in image_to_class_id:
+                    class_id = image_to_class_id[file_path]
+                    label = _CLASSES[class_id]
+                    words, boxes = self._path_to_OCR(image_to_OCR, file_path)
+                    a = dict(
+                        id=file_path,
+                        image={"path": file_path, "bytes": file_obj.read()},
+                        label=label,
+                        words=words,
+                        boxes=boxes,
+                    )
+                    from pdb import set_trace; set_trace()
+                    yield file_path, a

test_loader.py

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+from datasets import load_dataset
+
+data = load_dataset(
+    "./rvl_cdip_easyOCR.py",
+    split="test",
+    #cache_dir="/mnt/lerna/data/HFcache",
+    data_files={ # this is the path to the images if it does not download it
+        "binary": __file__#"/mnt/lerna/data/HFcache/downloads/c8cc6f89129255a9adf3e97e319ebe2055cf97662135b3ad26c79e9432544db5",
+    },
+    data_dir="/home/jordy/Downloads/OCRedText", # this is the path to the OCR data
+)
+