Converting Multiple Rows of Data in a Table to a Single Row Extracted through OCR

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In this article, we will explore how to convert multiple rows of data in a table extracted through Optical Character Recognition (OCR) into a single row. This can be achieved by identifying the pattern in the desired output and writing code to concatenate the lines till the next pattern.

Understanding OCR Output

The provided OCR output is a plain text representation of the original PDF document, where each line represents a separate entry in the table. The entries are separated by newline characters (\n). However, the actual table format information is not preserved in this flat text format.

Identifying the Pattern

To convert multiple rows to a single row, we need to identify the pattern that separates each individual entry. In this case, it appears that the first character of each line should be followed by whitespace and then the actual data starts.

We can use regular expressions (regex) to identify this pattern. The regex ^'[0-9]{2,3,4}' will match any line starting with a number between 2 and 4 digits long, followed by whitespace. This will help us distinguish between individual entries and concatenate them into a single row.

Regex Pattern Explanation

The regex pattern ^'[0-9]{2,3,4}' can be broken down as follows:

^ asserts the start of the line.
[ ] matches any whitespace character (including spaces, tabs, etc.).
'[0-9]' matches a single quote followed by a digit between 0 and 9.
{2,3,4} specifies that we want to match exactly 2, 3, or 4 digits.
' closes the quote.

This regex pattern will work for most of the lines in the desired output but might not cover all possible variations. We may need to adjust it based on specific requirements.

Code Solution

Here’s a sample Python code snippet that demonstrates how to convert multiple rows to a single row using the identified pattern:

import re

def convert_rows(input_file, output_file):
    with open(input_file, 'r') as f_in:
        lines = f_in.readlines()

    # Initialize an empty list to store the extracted data
    data_list = []

    # Loop through each line and extract the relevant data
    for line in lines:
        match = re.match(r'^\s*[0-9]{2,3,4}\s*(.*)', line)
        
        if match:
            # If a match is found, extract the relevant data and append it to the list
            data_list.append(match.group(1).strip())
    
    # Join the extracted data into a single string separated by whitespace
    output_string = ' '.join(data_list)

    with open(output_file, 'w') as f_out:
        f_out.write(output_string)

# Example usage:
convert_rows('input.txt', 'output.txt')

In this code:

We read the input file line by line.
For each line, we use the regex pattern to match and extract the relevant data (everything after the first number followed by whitespace).
If a match is found, we append it to our data_list.
Finally, we join the extracted data into a single string separated by whitespace and write it to the output file.

Note that this code assumes the input file has the same format as the provided OCR output. You may need to adjust the regex pattern or modify the code based on specific requirements.

Retaining Table Format Information

To retain table format information, we can use additional regular expressions to identify and preserve the relevant data. For example, we might want to match dates in a specific format (e.g., mm/dd/yyyy) and preserve them as-is.

However, this would require more complex regex patterns and potentially additional code to handle these variations. We can explore more advanced techniques for handling table format information in future articles.

Conclusion

In this article, we explored how to convert multiple rows of data in a table extracted through OCR into a single row using regular expressions. By identifying the pattern in the desired output and writing code to concatenate the lines till the next pattern, we can achieve this conversion efficiently.

We also discussed how to retain table format information by preserving relevant data such as dates or other critical fields. However, this would require more complex regex patterns and potentially additional code.

As with any OCR-based solution, there may be variations in input formats that require adjustments to the code. We will continue to explore and improve our techniques for handling these complexities in future articles.

Last modified on 2023-09-11