Update readme with new benchmark details

README.md

@@ -370,7 +370,7 @@ There are some settings that you may find useful if things aren't working the wa
 Pass the `debug` option to activate debug mode.  This will save images of each page with detected layout and text, as well as output a json file with additional bounding box information.
 
 # Benchmarks
-
+## Overall PDF Conversion
 Benchmarking PDF extraction quality is hard.  I've created a test set by finding books and scientific papers that have a pdf version and a latex source.  I convert the latex to text, and compare the reference to the output of text extraction methods.  It's noisy, but at least directionally correct.
 
 **Speed**
@@ -393,6 +393,13 @@ Marker takes about 6GB of VRAM on average per task, so you can convert 8 documen
 
 ![Benchmark results](data/images/per_doc.png)
 
+## Table Conversion
+Marker can extract tables from your PDFs using `marker.converters.table.TableConverter`. The table extraction performance is measured by comparing the extracted HTML representation of tables against the original HTML representations using the test split of [FinTabNet](https://developer.ibm.com/exchanges/data/all/fintabnet/). The HTML representations are compared using a [tree edit distance] based metric to judge both structure and content. Marker detects and identifies the structure of all tables in a PDF page and achieves an average score of `0.65` via this approach.
+
+|   Avg score |   Total tables |
+|-------------|----------------|
+|        0.65 |           1149 |
+
 ## Running your own benchmarks
 
 You can benchmark the performance of marker on your machine. Install marker manually with:
@@ -402,12 +409,21 @@ git clone https://github.com/VikParuchuri/marker.git
 poetry install
 ```
 
+### Overall PDF Conversion
+
 Download the benchmark data [here](https://drive.google.com/file/d/1ZSeWDo2g1y0BRLT7KnbmytV2bjWARWba/view?usp=sharing) and unzip. Then run the overall benchmark like this:
 
 ```shell
 python benchmarks/overall.py data/pdfs data/references report.json
 ```
 
+### Table Conversion
+The processed FinTabNet dataset is hosted [here](https://huggingface.co/datasets/datalab-to/fintabnet-test) and is automatically downloaded. Run the benchmark with:
+
+```shell
+python benchmarks/table/table.py table_report.json --max 1000
+```
+
 # Thanks
 
 This work would not have been possible without amazing open source models and datasets, including (but not limited to):

benchmarks/table/table.py

@@ -86,15 +86,14 @@ def main(out_file, dataset, max):
             print('Broken PDF, Skipping...')
             continue
 
-    total_time = time.time() - start
+    print(f"Total time: {time.time() - start}")
 
     with ThreadPoolExecutor(max_workers=16) as executor:
         results = list(tqdm(executor.map(update_teds_score, results), desc='Computing alignment scores', total=len(results)))
     avg_score = sum([r["score"] for r in results]) / len(results)
 
-    print(f"Total time: {time.time() - start}")
-    headers = ["Avg score", "Time per table", "Total tables"]
-    data = [f"{avg_score:.3f}", f"{total_time / len(results):.3f}", len(results)]
+    headers = ["Avg score", "Total tables"]
+    data = [f"{avg_score:.3f}", len(results)]
     table = tabulate([data], headers=headers, tablefmt="github")
     print(table)
     print("Avg score computed by comparing marker predicted HTML with original HTML")