I have a scanned PDF which has some random data in a tabular format and want to copy that into an Excel sheet.
I have played around with digital PDFs and use 'tabula' to extract tables but scanned PDFs require OCRs(what I've seen over google).
I know there is an OCR involved(tesseract), but do not know what approach should I take towards solving the problem.
Take a look at Tesseract's TSV (Tab Separated Value) output format and see if Excel can read or import it. Some tranformation may be needed to get it into a format consumable by Excel.
https://digi.bib.uni-mannheim.de/tesseract/manuals/tesseract.1.html
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I did some analysis of my .txt file using python. Each data produced a set of results. I need to transfer the whole results into a single excel file. You can see my results from this image enter image description here. Also, I want to mention each txt file name along with the results in the excel sheet. Can anyone help this matter ?
I presume that you are using pandas. Pandas have a build in function to export to excel.
df_excel.to_excel("output.xlsx")
If you want to add the name of the textfile, simply add the name to the corresponding rows.
I have 3 tables (image pasted) all 3 table(have same columns) look same and i want data of address column (yellow colour) of 3 tables stored inside a variable.
There are different ways to handle extraction of tables from pdf. The final solution will depend primarily on individual pdf that you need to read. Some variables to think about when choosing the solution are:
is the pdf just an image saved as pdf (rastered image of a scanned
document)?
what is the quality of pdf?
is there any noise in the pdf
files (e.g. spots caused by a printer) you need to get rid of?
is the table in pdf skewed?
how many pages has a pdf?
how many pages a table spans across?
how many documents do you need to scan?
There are many solutions to extract tables from pdf ranging from table-specialized OCR services to python utility libraries to help you build your own extraction program.
An example of a powerful tool to convert data from tables from pdf to excel is Camelot, which you have included in your question's tags. It abstracts a lot of complexity involved in the task at hand. You just install it
and access it for example like that:
import camelot
file = 'https://www.w3.org/WAI/WCAG21/working-examples/pdf-table/table.pdf'
tables = camelot.read_pdf(file)
tables[0].to_excel('table.xlsx')
As I mentioned, the devil lies in the individual characteristics of a table and a pdf file.
I need to recognize written text in a table and parse it in json. I do it using python. I don't really understand how to extract photos of text from a table that is in pdf format. Because the usual table recognizer is not suitable, since written text is not recognized there. Accordingly, I need to somehow cut the cells from the table, how to do this?
if you want to extract tables and their cells you probably need a table extractor like this; 1
Then after extracting the table and its cells with their coordinates, you are allowed to pick those pixels. For example; img[x1:x2,y1:y2]
After obtaining the cell's pixels, you can use the Tesseract OCR engine to understand the texts written in image pixels.
These are the general steps that you need to follow, I can help you more if you precise your question more.
PDF format has not 'table' and 'cell'.
Covert PDF into PNG format or other raster format and use OCR as say BlackCode.
I need to extract data from tables in multiple PDF's using Python. I have tested both camelot and tabula however neither of them are able to accurately get the data. The tables have some merged cells, cells with mutiple lines of information etc. so both these libraries get confused. Is there a good way of approaching this issue?
There may be something wrong with the underlying structure of the table encoded in the PDF if that's the case.
You could use OCR, and do some string/regex manipulation to extract column data from each row. github.com/cseas/ocr-table seems to work. See the input.pdf and output.txt to see if it works with your situation.
first of all, thank you for taking the time to help me!
I am currently working on a machine learning problem using python where I have to extract several specific sections in a large text file for training a classification algorithm. The texts then have to be saved in a CSV format with its corresponding ID-num and label/category from an excel sheet.
The CSV file should look like this: https://imgur.com/a/3cntJlL
The excel sheet contains a lot of columns where only the ID-number and label columns should be used.
Here you can see some of the excel sheet: https://imgur.com/a/AZlWdeE
IDNUM column is the ID-number which connects the excel sheet to a specific text.
The AType1 column is the corresponding label which also has to be saved.
Here you can see some of one of the text files: https://imgur.com/a/Yns8HAC
The text which should be extracted goes from the word "Text:" to where there are two "*" (stars) right after each other in two lines. The ID-num is placed above the section, as the picture shows.
I have been trying to split the document but I can seem to figure out how to make the CSV file containing information from both an excel sheet and the text file. It would be optimal to make a script that can do this in one run and maybe then loop through several large text files.
So, my problem is to create a script which can:
Match excel cell content (ID-number) with text
Extract a section of the text between two delimiters ("Text:" and "* \n *")
Save the text, ID-number and label in a CSV file.
I hope there is someone who can help me. I am on the beginner level of using python so making this kind of script is pretty challenging.
Looking forward to hearing your ideas!
// Rasmus
It would be good for you to familiarize yourself with the pandas library.
Pandas (https://pandas.pydata.org/docs/) will allow you to read a CSV file into what is called a dataframe and manipulate the data by column name and rows. You can also put your results into a pandas dataframe and write the results to a CSV file.