Last updated 10 February 2024
Note: Google Document AI’s processors are gradually getting better. When you process the documents in this vignette, you may not encounter the same column-reading problem as I did when I wrote it. But the general principles and techniques are transferable to other cases where column-reading problems do arise.
Google Document AI (DAI) has excellent character recognition, but often reads columns wrong. This vignette will show you how to identify and reorder jumbled text with the tools in the
Server-based OCR engines such as Google Document AI and Amazon
Textract represent a major advance in OCR technology. They handle visual
noise extremely well and effectively eliminate the need for image
preprocessing, the most agonizing part of OCR in
and other standalone libraries. DAI also reads non-Western languages
such as Arabic better than any other general engine I have seen.
But DAI and Textract still struggle with text columns and irregular page layouts. In my experience, DAI will misread a multi-column page about a third of the time, and the error rate increases with the complexity of the layout. This is not a problem if you plan to apply “bag-of-words” text mining techniques, but if you’re looking at Natural Language Processing or actually reading the text, you cannot trust Document AI or Textract to always return accurate text.
DAI column-reading errors are of two main types. The first is to put
text blocks in the wrong order, and the second is to merge blocks that
shouldn’t be merged. Both errors can be corrected programmatically with
the tools in the
Note that the solutions proposed here are not the only ways to
address this problem. An alternative approach consists of adopting a
two-stage procedure whereby you first use an object detection model such
as LayoutParser to
identify individual columns of text, the images of which you then pass
on to Document AI for character recognition. But this involves Python
and is beyond the scope of what
daiR alone can do.
To illustrate the problem, let’s feed DAI a simple two-column text. This one is from the CIA’s archive of declassified intelligence documents:
We start by uploading it to Google Storage and telling Document AI to
process the document from there.
dai_notify() will beep
when the processing is done.
Then we search for a JSON file containing the string “CIA_columns” and download it.
Then we extract the text.
On first inspection, this does not look so bad. But notice the transition from the first to the second paragraph:
… they might reduce public support for the new Dubcek administration. Czech consumer in connection with these price increases.
Something’s not right. Could it be a column-reading error?
We can find out with the function
extracts boundary box data from the
.json file and draws
numbered rectangles on an image of each page of the source document.
(The images come from the json file, where they are stored as
Check your temporary directory (type
tempdir() for the
path) for a file ending in
_blocks.png and pull it up:
We can immediately see that the blocks are in the wrong order. How to fix this?
.json file from DAI comes with a ton of
data that allow us to programmatically reorder the text. The key is to
generate a token dataframe with page location data and then filter and
reorder as necessary. We create the dataframe with
The dataframe has the words in the order in which DAI proposes to
read them, and the
block column has the number of the block
to which each word belongs. This allows us to reorder the blocks while
keeping the within-block word order intact.
We see from the annotated image that the real order of the blocks should be 1 - 2 - 3 - 5 - 7 - 4 - 6. We can store this in a vector that we use to reorder the dataframe.
We get the correct text from the
Now the transition from the first to the second paragraph makes more sense:
A more complex — and, unfortunately, more common — situation is when DAI fails to distinguish between columns. This means that lines do not end where they should, resulting in long stretches of incomprehensible text. We can illustrate this with an article about the great Peshtigo forest fire in Wisconsin in 1871, available on the Internet Archive.
We do our processing routine again:
This time we’ll skip the text printout and go straight to inspecting the boundary boxes:
As we can see, this time DAI has failed to distinguish between the two main columns. We can verify this by checking the beginning of the text:
This means that we must find a way of splitting block 12 vertically.
What we will do is create a new boundary box that captures only the right-hand column. Then we will feed the location coordinates of the new box back into the token dataframe so that the tokens that fall within it are assigned a new block number. We can then reorder the blocks as we did in the previous example.
There are two main ways to obtain the coordinates of a new block: mathematically or through image annotation.
We can split blocks mathematically by using the location data for existing blocks in the json file. We start by building a block dataframe to keep track of the blocks.
Then we use the function
split_block() to cut block 12
vertically in half. This function takes as input a block dataframe, the
page and number of the block to split, and a parameter
cut_point, which is a number from 1 to 99 for the relative
location of the cut point.
split_block() returns a new
block dataframe that includes the new block and revised coordinates for
the old one.
If we had more blocks to split, we could repeat the procedure as many
times as necessary. We just have to make sure to feed the latest version
of the block dataframe into the
When we have a block dataframe that captures the layout fairly
accurately, we can use the
reassign_tokens() function to
assign new block values to the words in the token dataframe.
reassign_tokens() takes as input the token dataframe and
the new block dataframe and returns a revised token dataframe.
In this particular case, the blocks are in the right order after splitting, so we can extract a correct text right away. In other cases the blocks may need reordering, in which case we use the procedure from the previous section.
Mathematical splitting will often be the easiest method, and it can be particularly efficient when you have a lot of documents with the exact same column structure. However, it may sometimes be difficult to tell with the naked eye where the cut point should be. At other times the space between columns may be so narrow as to make precision important. For these situations we can use manual image annotation.
In principle you can use any image annotation tool, so long as you
format the resulting coordinates in a way that
processing functions understand. In the following, I will use labelme because it’s
easy to use and
daiR has a helper function for it.
Labelme opens from the command line, but has a fairly intuitive
graphical user interface. We load the annotated image generated by
draw_blocks(), click “create polygons” in the left pane,
right-click while the cursor is in the page pane, and choose “create
Then we mark the right-hand column and label it 13 (for the number of the new block).
Click “save” and store the json file, for example as
peshtigo1_blocks.json. Now we can load it in R and get the
coordinates of the new block 13 with the function
from_labelme(). This function returns a one-row dataframe
formatted like block dataframes generated with
We can then assign a new block number to the tokens that fall within
block13. For this we use
reassigns tokens on a specified page according to the coordinates of a
single new block.
Now we just need to reorder the token data frame by blocks, and the words will be in the right order. In this particular case, we do not need to supply a custom block order, since the block numbering reflects the right order of the text.
And again we have a text in the right order.