Update README.md

README.md

@@ -35,9 +35,9 @@ task_ids: []
 
 # Dataset Card for Pages of Early Soviet Performance (PESP)
 
-This dataset was created as part of the [Early Soviet Performance](https://cdh.princeton.edu/projects/pages-early-soviet-performance/) project at Princeton and is an effort to generate useful research data from a previously scanned [collection of illustrated periodicals](https://dpul.princeton.edu/slavic/catalog?f%5Breadonly_collections_ssim%5D%5B%5D=Russian+Illustrated+Periodicals) held by Princeton's Firestone Library.  Our work focused on document segmentation and the prediction of images, text, and mixed text in the document images.  The mixedtext category refers to segments where the typeface and text layout are mixed with other visual elements.  At a practical level, this category identifies sections that present problems for OCR. It also highlights the experimental use of text, images, and other elements by the editors and has research value.
+This dataset was created as part of the [Early Soviet Performance](https://cdh.princeton.edu/projects/pages-early-soviet-performance/) project at Princeton and is an effort to generate useful research data from a previously scanned [collection of illustrated periodicals](https://dpul.princeton.edu/slavic/catalog?f%5Breadonly_collections_ssim%5D%5B%5D=Russian+Illustrated+Periodicals) held by Princeton's Firestone Library.  Our work focused on document segmentation and the prediction of images, text, titles, and mixed text in the document images.  The mixedtext category refers to segments where the typeface and text layout are mixed with other visual elements. This category identifies sections that present problems for OCR and also highlights the experimental use of text, images, and other elements in the documents.
 
-For each of the ten journals of interest in Princeton's digital collections (DPUL), we identified the IIIF manifest URI.  Using those manifests, we downloaded each of the 24,000 document images. The [`IIIF_URIs.json`](https://huggingface.co/datasets/ajanco/pesp/blob/main/IIIF_URIs.json) file in this repository can be used to fetch the images from the Princeton Library IIIF servers.       
+For each of the ten journals of interest in Princeton's digital collections (DPUL), we identified the IIIF manifest URI.  Using those manifests, we downloaded each of the 24,000 document images.
 
 ## Journal manifests
 -  [Эрмитаж](https://figgy.princeton.edu/concern/scanned_resources/6b561fbb-ba28-4afb-91d2-d77b8728d7d9/manifest?manifest=https://figgy.princeton.edu/concern/scanned_resources/6b561fbb-ba28-4afb-91d2-d77b8728d7d9/manifest)
@@ -54,28 +54,36 @@ For each of the ten journals of interest in Princeton's digital collections (DPU
 
 ## Model 
 
-Using [makesense.ai](https://www.makesense.ai/) and a custom active learning application called ["Mayakovsky"](https://github.com/CDH-ITMO-Periodicals-Project/mayakovsky) we generated training data to add the new labels to a [YOLOv5 model](https://docs.ultralytics.com/tutorials/train-custom-datasets/).
+Using [makesense.ai](https://www.makesense.ai/) and a custom active learning application called ["Mayakovsky"](https://github.com/CDH-ITMO-Periodicals-Project/mayakovsky) we generated training data for a [YOLOv5 model](https://docs.ultralytics.com/tutorials/train-custom-datasets/).  The model was fine-tuned on the new labels and predictions were generated for all images in the collection. 
+
+## OCR 
+
+Using the model's predictions for image, title, text and mixedtext segments, we cropped using the bounding box and ran OCR on that document segment using Tesseract, Google Vision, and ABBYY FineReader Engine.  Given that the output of these various OCR engines can be difficult to compare, the document segments give a common denominator for comparison of OCR outputs.  Having three variations of the extracted text can be useful for experiments with OCR post-correction.
 
 ## Dataset 
 
-With the trained and fine-tuned model, we generated predictions for each of the images in the collection. The dataset contains an entry for each image with the following fields: 
-- filename, the image name (ex. 'Советский театр_1932 No. 4_16') with journal name, year, issue, page.
-- dpul, the URL for the image's journal in Digital Princeton University Library
-- journal, the journal name
-- year, the year of the journal issue
-- issue, the issue for the image
-- URI, the IIIF URI used to fetch the image from Princeton's IIIF server 
-- yolo, the raw model prediction (ex '3 0.1655 0.501396 0.311'), in Yolo's normalized xywh format (<object-class> <x> <y> <width> <height>). The labels are 'image'=0, 'mixedtext'=1, 'title'=2, 'textblock'=3.
-- yolo_predictions, a List with a dictionary for each of the model's predictions with fields for: 
-  -  label, the predicted label
-  - x, the x-value location of the center point of the prediction 
-  - y, the y-value location of the center point of  the prediction
-  - w, the total width of the prediction's bounding box 
-  - h, the total height of the prediction's bounding box
-  - abbyy_text, the text extracted from the predicted document segment using ABBY FineReader. Note that due to costs, only about 800 images have this data
-  - tesseract_text, the text extracted from the predicted document segment using Tesseract.
-  - vision_text, the text extracted from the predicted document segment using Google Vision.
-
-
-
-'filename', 'dpul', 'journal', 'year', 'issue', 'uri', 'yolo', 'yolo_predictions', 'text', 'images_meta'
+The dataset contains an entry for each image with the following fields: 
+- filename: the image name (ex. 'Советский театр_1932 No. 4_16') with journal name, year, issue, page.
+- dpul: the URL for the image's journal in Digital Princeton University Library
+- journal: the journal name
+- year: the year of the journal issue
+- issue: the issue for the image
+- URI: the IIIF URI used to fetch the image from Princeton's IIIF server 
+- yolo: the raw model prediction (ex '3 0.1655 0.501396 0.311'), in Yolo's normalized xywh format (<object-class> <x> <y> <width> <height>). The labels are 'image'=0, 'mixedtext'=1, 'title'=2, 'textblock'=3.
+- yolo_predictions: a List with a dictionary for each of the model's predictions with fields for: 
+  - label: the predicted label
+  - x: the x-value location of the center point of the prediction 
+  - y: the y-value location of the center point of  the prediction
+  - w: the total width of the prediction's bounding box 
+  - h: the total height of the prediction's bounding box
+  - abbyy_text: the text extracted from the predicted document segment using ABBY FineReader. Note that due to costs, only about 800 images have this data
+  - tesseract_text: the text extracted from the predicted document segment using Tesseract.
+  - vision_text: the text extracted from the predicted document segment using Google Vision.
+
+# Useage 
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset('ajanco/pesp')
+```