Corrections of OCRd text in Trove's newspapers¶
The full text of newspaper articles in Trove is extracted from page images using Optical Character Recognition (OCR). The accuracy of the OCR process is influenced by a range of factors including the font and the quality of the images. Many errors slip through. Volunteers have done a remarkable job in correcting these errors, but it's a huge task. This notebook explores the scale of OCR correction in Trove.
There are two ways of getting data about OCR corrections using the Trove API. To get aggregate data you can include has:corrections in your query to limit the results to articles that have at least one OCR correction.
To get information about the number of corrections made to the articles in your results, you can add the reclevel=full parameter to include the number of corrections and details of the most recent correction to the article record. For example, note the correctionCount and lastCorrection values in the record below:
{
"article": {
"id": "41697877",
"url": "/newspaper/41697877",
"heading": "WRAGGE AND WEATHER CYCLES.",
"category": "Article",
"title": {
"id": "101",
"value": "Western Mail (Perth, WA : 1885 - 1954)"
},
"date": "1922-11-23",
"page": 4,
"pageSequence": 4,
"troveUrl": "https://trove.nla.gov.au/ndp/del/article/41697877",
"illustrated": "N",
"wordCount": 1054,
"correctionCount": 1,
"listCount": 0,
"tagCount": 0,
"commentCount": 0,
"lastCorrection": {
"by": "*anon*",
"lastupdated": "2016-09-12T07:08:57Z"
},
"identifier": "https://nla.gov.au/nla.news-article41697877",
"trovePageUrl": "https://trove.nla.gov.au/ndp/del/page/3522839",
"pdf": "https://trove.nla.gov.au/ndp/imageservice/nla.news-page3522839/print"
}
}
Setting things up¶
In [1]:
import requests
import os
import ipywidgets as widgets
from operator import itemgetter # used for sorting
import pandas as pd # makes manipulating the data easier
import altair as alt
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
from tqdm.auto import tqdm
from IPython.display import display, HTML, FileLink, clear_output
import math
from collections import OrderedDict
import time
# Make sure data directory exists
os.makedirs('data', exist_ok=True)
# Create a session that will automatically retry on server errors
s = requests.Session()
retries = Retry(total=5, backoff_factor=1, status_forcelist=[ 502, 503, 504 ])
s.mount('http://', HTTPAdapter(max_retries=retries))
s.mount('https://', HTTPAdapter(max_retries=retries))
In [2]:
# Are you using JupyterLab or the classic Jupyter notebook?
# If you're using the classic notebook, uncomment the following line to make sure Altair charts appear as expected!
alt.renderers.enable('notebook')
Out[2]:
In [57]:
api_key = ''
print('Your API key is: {}'.format(api_key))
In [4]:
# Basic parameters for Trove API
params = {
'facet': 'year', # Get the data aggregated by year.
'zone': 'newspaper',
'key': api_key,
'encoding': 'json',
'n': 0 # We don't need any records, just the facets!
}
In [5]:
def get_results(params):
'''
Get JSON response data from the Trove API.
Parameters:
params
Returns:
JSON formatted response data from Trove API
'''
response = s.get('https://api.trove.nla.gov.au/v2/result', params=params, timeout=30)
response.raise_for_status()
# print(response.url) # This shows us the url that's sent to the API
data = response.json()
return data
How many newspaper articles have corrections?¶
Let's find out what proportion of newspaper articles have at least one OCR correction.
First we'll get to the total number of newspaper articles in Trove.
In [6]:
# Set the q parameter to a single space to get everything
params['q'] = ' '
# Get the data from the API
data = get_results(params)
# Extract the total number of results
total = int(data['response']['zone'][0]['records']['total'])
print('{:,}'.format(total))
Now we'll set the q parameter to has:corrections to limit the results to newspaper articles that have at least one correction.
In [7]:
# Set the q parameter to 'has:corrections' to limit results to articles with corrections
params['q'] = 'has:corrections'
# Get the data from the API
data = get_results(params)
# Extract the total number of results
corrected = int(data['response']['zone'][0]['records']['total'])
print('{:,}'.format(corrected))
Calculate the proportion of articles with corrections.
In [8]:
print('{:.2%} of articles have at least one correction'.format(corrected/total))
You might be thinking that these figures don't seem to match the number of corrections by individuals displayed on the digitised newspapers home page. Remember that these figures show the number of articles that include corrections, while the individual scores show the number of lines corrected by each volunteer.
Number of corrections by year¶
In [9]:
def get_facets(data):
'''
Loop through facets in Trove API response, saving terms and counts.
Parameters:
data - JSON formatted response data from Trove API
Returns:
A list of dictionaries containing: 'term', 'total_results'
'''
facets = []
try:
# The facets are buried a fair way down in the results
# Note that if you ask for more than one facet, you'll have use the facet['name'] param to find the one you want
# In this case there's only one facet, so we can just grab the list of terms (which are in fact the results by year)
for term in data['response']['zone'][0]['facets']['facet']['term']:
# Get the year and the number of results, and convert them to integers, before adding to our results
facets.append({'term': term['search'], 'total_results': int(term['count'])})
# Sort facets by year
facets.sort(key=itemgetter('term'))
except TypeError:
pass
return facets
def get_facet_data(params, start_decade=180, end_decade=201):
'''
Loop throught the decades from 'start_decade' to 'end_decade',
getting the number of search results for each year from the year facet.
Combine all the results into a single list.
Parameters:
params - parameters to send to the API
start_decade
end_decade
Returns:
A list of dictionaries containing 'year', 'total_results' for the complete
period between the start and end decades.
'''
# Create a list to hold the facets data
facet_data = []
# Loop through the decades
for decade in tqdm(range(start_decade, end_decade + 1)):
print(params)
# Avoid confusion by copying the params before we change anything.
search_params = params.copy()
# Add decade value to params
search_params['l-decade'] = decade
# Get the data from the API
data = get_results(search_params)
# Get the facets from the data and add to facets_data
facet_data += get_facets(data)
# Reomve the progress bar (you can also set leave=False in tqdm, but that still leaves white space in Jupyter Lab)
clear_output()
return facet_data
In [10]:
facet_data = get_facet_data(params)
In [11]:
# Convert our data to a dataframe called df
df = pd.DataFrame(facet_data)
In [12]:
df.head()
Out[12]:
In [13]:
# Reset the 'q' parameter
# Use a an empty search (a single space) to get ALL THE ARTICLES
params['q'] = ' '
# Get facet data for all articles
all_facet_data = get_facet_data(params)
In [14]:
# Convert the results to a dataframe
df_total = pd.DataFrame(all_facet_data)
In [15]:
def merge_df_with_total(df, df_total, how='left'):
'''
Merge dataframes containing search results with the total number of articles by year.
This is a left join on the year column. The total number of articles will be added as a column to
the existing results.
Once merged, do some reorganisation and calculate the proportion of search results.
Parameters:
df - the search results in a dataframe
df_total - total number of articles per year in a dataframe
Returns:
A dataframe with the following columns - 'year', 'total_results', 'total_articles', 'proportion'
(plus any other columns that are in the search results dataframe).
'''
# Merge the two dataframes on year
# Note that we're joining the two dataframes on the year column
df_merged = pd.merge(df, df_total, how=how, on='term')
# Rename the columns for convenience
df_merged.rename({'total_results_y': 'total_articles'}, inplace=True, axis='columns')
df_merged.rename({'total_results_x': 'total_results'}, inplace=True, axis='columns')
# Set blank values to zero to avoid problems
df_merged['total_results'] = df_merged['total_results'].fillna(0).astype(int)
# Calculate proportion by dividing the search results by the total articles
df_merged['proportion'] = df_merged['total_results'] / df_merged['total_articles']
return df_merged
In [16]:
# Merge the search results with the total articles
df_merged = merge_df_with_total(df, df_total)
df_merged.head()
Out[16]:
In [17]:
chart1 = alt.Chart(df).mark_line(point=True).encode(
x=alt.X('term:Q', axis=alt.Axis(format='c', title='Year')),
y=alt.Y('total_results:Q', axis=alt.Axis(format=',d', title='Number of articles')),
tooltip=[alt.Tooltip('term:Q', title='Year'), alt.Tooltip('total_results:Q', title='Articles', format=',')]
).properties(width=700, height=250)
chart2 = alt.Chart(df_merged).mark_line(point=True, color='red').encode(
x=alt.X('term:Q', axis=alt.Axis(format='c', title='Year')),
# This time we're showing the proportion (formatted as a percentage) on the Y axis
y=alt.Y('proportion:Q', axis=alt.Axis(format='%', title='Proportion of articles')),
tooltip=[alt.Tooltip('term:Q', title='Year'), alt.Tooltip('proportion:Q', title='Proportion', format='%')],
# Make the charts different colors
color=alt.value('orange')
).properties(width=700, height=250)
# This is a shorthand way of stacking the charts on top of each other
chart1 & chart2
Out[17]:
Number of corrections by category¶
In [18]:
params['q'] = 'has:corrections'
params['facet'] = 'category'
In [19]:
data = get_results(params)
facets = []
for term in data['response']['zone'][0]['facets']['facet']['term']:
# Get the state and the number of results, and convert it to integers, before adding to our results
facets.append({'term': term['search'], 'total_results': int(term['count'])})
df_categories = pd.DataFrame(facets)
In [20]:
df_categories.head()
Out[20]:
In [21]:
params['q'] = ' '
data = get_results(params)
facets = []
for term in data['response']['zone'][0]['facets']['facet']['term']:
# Get the state and the number of results, and convert it to integers, before adding to our results
facets.append({'term': term['search'], 'total_results': int(term['count'])})
df_total_categories = pd.DataFrame(facets)
In [22]:
df_categories_merged = merge_df_with_total(df_categories, df_total_categories)
df_categories_merged
Out[22]:
In [ ]:
In [23]:
df_categories_filtered = df_categories_merged.loc[df_categories_merged['total_articles'] > 5000]
df_categories_filtered
Out[23]:
In [24]:
cat_chart1 = alt.Chart(df_categories_filtered).mark_bar().encode(
x=alt.X('term:N', title='Category'),
y=alt.Y('total_results:Q', title='Articles with corrections')
)
cat_chart2 = alt.Chart(df_categories_filtered).mark_bar().encode(
x=alt.X('term:N', title='Category'),
y=alt.Y('proportion:Q', axis=alt.Axis(format='%', title='Proportion of articles with corrections')),
)
cat_chart1 | cat_chart2
Out[24]:
Number of corrections by newspaper¶
In [25]:
params['q'] = 'has:corrections'
params['facet'] = 'title'
In [26]:
data = get_results(params)
facets = []
for term in data['response']['zone'][0]['facets']['facet']['term']:
# Get the state and the number of results, and convert it to integers, before adding to our results
facets.append({'term': term['search'], 'total_results': int(term['count'])})
df_newspapers = pd.DataFrame(facets)
In [27]:
df_newspapers.head()
Out[27]:
Get the total number of articles per newspaper.
In [28]:
params['q'] = ' '
In [29]:
data = get_results(params)
facets = []
for term in data['response']['zone'][0]['facets']['facet']['term']:
# Get the state and the number of results, and convert it to integers, before adding to our results
facets.append({'term': term['search'], 'total_results': int(term['count'])})
df_newspapers_total = pd.DataFrame(facets)
In [30]:
df_newspapers_merged = merge_df_with_total(df_newspapers, df_newspapers_total, how='right')
In [31]:
df_newspapers_merged.sort_values(by='proportion', ascending=False, inplace=True)
df_newspapers_merged.rename(columns={'term': 'id'}, inplace=True)
In [32]:
df_newspapers_merged.head()
Out[32]:
Let's get the names of the newspapers.
In [33]:
title_params = {
'key': api_key,
'encoding': 'json',
}
title_data = s.get('https://api.trove.nla.gov.au/v2/newspaper/titles', params=params).json()
In [34]:
titles = []
for newspaper in title_data['response']['records']['newspaper']:
titles.append({'title': newspaper['title'], 'id': int(newspaper['id'])})
df_titles = pd.DataFrame(titles)
In [35]:
df_titles.head()
Out[35]:
In [36]:
df_titles.shape
Out[36]:
Exclude the government gazettes.
In [37]:
gazette_data = s.get('https://api.trove.nla.gov.au/v2/gazette/titles', params=params).json()
gazettes = []
for gaz in gazette_data['response']['records']['newspaper']:
gazettes.append({'title': gaz['title'], 'id': int(gaz['id'])})
df_gazettes = pd.DataFrame(gazettes)
In [38]:
df_gazettes.shape
Out[38]:
In [39]:
df_titles_not_gazettes = df_titles[~df_titles['id'].isin(df_gazettes['id'])]
In [40]:
df_newspapers_with_titles = pd.merge(df_titles_not_gazettes, df_newspapers_merged, how='left', on='id').fillna(0).sort_values(by='proportion', ascending=False)
In [41]:
# Convert the totals back to integers
df_newspapers_with_titles[['total_results', 'total_articles']] = df_newspapers_with_titles[['total_results', 'total_articles']].astype(int)
Most corrected.
In [42]:
df_newspapers_with_titles[:25]
Out[42]:
Least corrected.
In [43]:
df_newspapers_with_titles[-25:]
Out[43]:
In [44]:
df_newspapers_with_titles_csv = df_newspapers_with_titles.copy()
df_newspapers_with_titles_csv.rename({'total_results': 'articles_with_corrections'}, axis=1, inplace=True)
df_newspapers_with_titles_csv['percentage_with_corrections'] = df_newspapers_with_titles_csv['proportion'] * 100
df_newspapers_with_titles_csv.sort_values(by=['percentage_with_corrections'], inplace=True)
df_newspapers_with_titles_csv[['id', 'title', 'articles_with_corrections', 'total_articles', 'percentage_with_corrections']].to_csv('titles_corrected.csv', index=False)
#df_newspapers_with_titles.to_csv('titles_corrected.csv', index=False)
In [56]:
display(FileLink('titles_corrected.csv'))
Neediest newspapers¶
Let's see if we can combine some guesses about OCR error rates with the correction data to find the newspapers most in need of help.
To make a guesstimate of error rates, we'll use the occurance of 'tbe' – ie a common OCR error for 'the'. I don't know how valid this is, but it's a place to start.
In [45]:
params['q'] = 'text:"tbe"~0'
params['facet'] = 'title'
In [46]:
data = get_results(params)
facets = []
for term in data['response']['zone'][0]['facets']['facet']['term']:
# Get the state and the number of results, and convert it to integers, before adding to our results
facets.append({'term': term['search'], 'total_results': int(term['count'])})
df_errors = pd.DataFrame(facets)
In [47]:
df_errors_merged = merge_df_with_total(df_errors, df_newspapers_total, how='right')
df_errors_merged.sort_values(by='proportion', ascending=False, inplace=True)
df_errors_merged.rename(columns={'term': 'id'}, inplace=True)
In [48]:
df_errors_merged.head()
Out[48]:
In [49]:
df_errors_with_titles = pd.merge(df_titles_not_gazettes, df_errors_merged, how='left', on='id').fillna(0).sort_values(by='proportion', ascending=False)
In [50]:
df_errors_with_titles[:25]
Out[50]:
In [51]:
df_errors_with_titles[-25:]
Out[51]:
In [52]:
corrections_errors_merged_df = pd.merge(df_newspapers_with_titles, df_errors_with_titles, how='left', on='id')
In [53]:
corrections_errors_merged_df.head()
Out[53]:
In [54]:
corrections_errors_merged_df['proportion_uncorrected'] = corrections_errors_merged_df['proportion_x'].apply(lambda x: 1 - x)
corrections_errors_merged_df.rename(columns={'title_x': 'title', 'proportion_x': 'proportion_corrected', 'proportion_y': 'proportion_with_errors'}, inplace=True)
corrections_errors_merged_df.sort_values(by=['proportion_with_errors', 'proportion_uncorrected'], ascending=False, inplace=True)
In [55]:
corrections_errors_merged_df[['title', 'proportion_with_errors', 'proportion_uncorrected']][:25]
Out[55]:
In [ ]: