I am working on invoice parser which extracts data from invoices in pdf or image format.It works on simple pdf with non tabular data but gives lots of output data to process with pdf which contains tables.I am not able to get a working generic solution for this.I have tried the following libraries
Invoice2Data : It is based on templates.It has given fairly good results in json format till now.But Template creation for complex pdfs containing dynamic table is complex.
Tabula : Table extraction is based on coordinates of the table to be extracted.If the data in the table increases the table length increases and hence the coordinates changes.So in this case it gives wrong results.
Pdftotext : It converts any pdfs to text but with the format that needs lots of parsing which we do not want.
Aws_Textract and Elis_Rossum_Ai : Gives all the data in json format.But if the table column contains multiple line then json parsing becomes difficult.Even the json given is huge in size to parse.
Tesseract : Same as pdftotext.Complex pdfs are not parseable.
Other than all this or with combination of the above libraries has anyone been able to parse complex pdf data please help.
I am working on a similar business problem. since invoices don't have fixed format so you can't directly use any text parsing method.
To solve this problem you have to use Computer Vision (Deep Learning) for field detection and Pytesseract OCR for converting image into text. For better understanding here are the steps:
Convert invoices to image and annotate the images with fields like address, Amount etc using tools like labelImg. (For better results use different types of 500-1000 invoices)
After Generating XML files train any object detection model like YOLO or TF object detection API.
The model will detect the fields and gives you coordinates of Region Of Interest(ROI). like
Apply Pytessract OCR on the ROI coordinates. Click Here
Finally, use regex to validate the text in the extracted field and perform any manipulation/transformation that is necessary. At last store data to CSV OR Database.
Hope my answer helps you! Upvote answer so it reaches to maximum people.
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I've got a large number of 3- and 6-column journal and newspaper pages that I want to OCR. I want to automate recognition of columns.
I've used tesseract (see a previous question) and Google Cloud Document AI (using the R package daiR) without great success.
These programs read the text very well, but do not do a good job of recognizing the column format of pages.
Here's a couple of examples from daiR:
Obviously these are complex images with some double columns and some tables inside columns. What I want is for the OCR to try to look for 6 columns.
I get good results if I preprocess images (for instance by cropping them into single columns or adding vertical lines), but I haven't found an efficient way to do this in large batches. Is there a way of preprocessing images or telling OCR programs to look for a given number of columns?
I have a system that extracts text from pictures ((MS word, tables, ..) documents with text more than 2 lines for example), but I do not want pictures that obviously have no text in it (such as textures, photos, ...). I've tried algorithm's from detect-text-in-images-with-opencv and detect-text-region-in-image-using-opencv, but all algorithms works not so good for example with these images
OpenCV has a module containing text detection.
https://docs.opencv.org/master/da/d56/group__text__detect.html
the module comes with sample code and documentation.
there is also a method based on deep learning called "EAST". here's the sample:
https://github.com/opencv/opencv/blob/master/samples/dnn/text_detection.cpp
I have a form as an image below and I want to extract all information including printed text (book, ID) and number handwriting text ( number of orders) as a txt file.
Does anyone can suggest me what is the best solution?
My current idea is:
Using deep learning to get location of objects from each column.
Apply Tesseract to extract text from these objects. ( does any better lib?)
Using deep learning to reconize handwriting text
You can use a text localisation model called EAST to get extract text from a image.
https://github.com/argman/EAST
And then you can use one of those OCR models to transcribe the text.
I often work with scanned papers. The papers contain tables (similar to Excel tables) which I need to type into the computer manually. To make the task worse the tables can be of different number of columns. Manually entering them into Excel is mundane to say the least.
I thought I can save myself a week of work if I can put a program to OCR it. Would it be possible to detect headers text areas with the OpenCV and OCR the text behind the detected image coordinates.
Can I achieve this with the help of OpenCV or do I need entirely different approach?
Edit: Example table is really just a standard table similar to what you can see in Excel and other spread-sheet applications, see below.
This question seems a little old but i was also working on a similar problem and got my own solution which i am explaining here.
For reading text using any OCR engine there are many challanges in getting good accuracy which includes following main cases:
Presence of noise due to poor image quality / unwanted elements/blobs in the background region. This will require some pre-processing like noise removal which can be easily done using gaussian filter or normal median filter methods. These are also available in opencv.
Wrong orientation of image: Because of wrong orientation OCR engine fails to segment the lines and words in image correctly which gives the worst accuracy.
Presence of lines: While doing word or line segmentation OCR engine sometimes also tries to merge the words and lines together and thus processing wrong content and hence giving wrong results.
There are other issues also but these are the basic ones.
In this case i think the scan image quality is quite good and simple and following steps can be used solve the problem.
Simple image binarization will remove the background content leaving only necessary content as shown here.
Now we have to remove lines which in this case is tabular grid. This can also be identified using connected components and removing the large connected components. So our final image that is needed to be fed to OCR engine will look like this.
For OCR we can use Tesseract Open Source OCR Engine. I got following results from OCR:
Caption title
header! header2 header3
row1cell1 row1cell2 row1cell3
row2cell1 row2cell2 row2cell3
As we can see here that result is quite accurate but there are some issues like
header! which should be header1, this is because OCR engine misunderstood ! with 1. This problem can be solved by further processing the result using Regex based operations.
After post processing the OCR result it can be parsed to read the row and column values.
Also here in this case to classify the sheet title, heading and normal cell values their font information can be used.
I am looking to do some text analysis in a program I am writing. I am looking for alternate sources of text in its raw form similar to what is provided in the Wikipedia dumps (download.wikimedia.com).
I'd rather not have to go through the trouble of crawling websites, trying to parse the html , extracting text etc..
What sort of text are you looking for?
There are many free e-books (fiction and non-fiction) in .txt format available at Project Gutenberg.
They also have large DVD images full of books available for download.
NLTK provides a simple Python API to access many text corpora, including Gutenberg, Reuters, Shakespeare, and others.
>>> from nltk.corpus import brown
>>> brown.words()
['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]
the gutenberg project has huge amounts of ebooks in various formats (including plain text)