When working with page images, however, it is very useful to be able to see pictures. Using a window managerĪs with earlier posts, we are going to use command line tools to process our files.
Older fonts and texts, or warped, indistinct or blurry page images often result in lower quality OCR. These will certainly have some errors, but the quality tends to be surprisingly good for clean scans of recently typed or printed pages. Starting with digital photographs or scans of documents, we can apply optical character recognition (OCR) to create machine-readable texts. In this post we focus on a preliminary issue: converting images of texts into text files that we can work with. The below code can be used for marking the regions of interest in the image and getting their respective co-ordinates.In previous posts, we looked at a variety of Linux command line techniques for analyzing text and finding patterns in it, including word frequencies, permuted term indexes, regular expressions, simple search engines and named entity recognition. In our case we will be trying to extract information from an invoice using the exact same approach. Through this approach, we can get maximum correct results for any given document. The only catch to this question is sometimes there are hidden line breaks/ page breaks that are embedded in the document and if this document is passed directly into the OCR engine, the continuity of data breaks automatically (as line breaks are recognized by OCR). The simple answer to this question is that YOU CAN Most of us would think to this point - why should we mark the regions in an image before doing OCR and not doing it directly? After marking those regions with the rectangle, we will crop those regions one by one from the original image before feeding it to the OCR engine. Here in this step we will mark the regions of the image from where we have to extract the data. Note: Before marking regions make sure that you have preprocessed the image for improving its quality (DPI ≥ 300, Skewness, Sharpness and Brightness should be adjusted, Thresholding etc.) Marking Regions of Image for Information Extraction Please refer to the below resources for downloading and installation instructions for Poppler.Īfter installation, any pdf can be converted to images using the below code.Īfter converting the PDF to images, the next step is to highlight the regions of the images from which we have to extract the information. Note: pdf2image uses Poppler which is a PDF rendering library based on the xpdf-3.0 code base and will not work without it. The following command can be used for installing the pdf2image library using pip installation method.
Pdf2image is a python library which converts PDF to a sequence of PIL Image objects using pdftoppm library. The libraries that I used for developing this solution were pdf2image (for converting PDF to images), OpenCV (for Image pre-processing) and finally PyTesseract for OCR along with Python. I came across a similar set of problem a few days back and wanted to share with you all the approach through which I solved this problem. However there are many factors due to which most of the people want to solve this problem using Open Source Libraries. There are various tools that are available in the market that can be used to perform this task. The process of extracting information from a digital copy of invoice can be a tricky task. Document Intelligence using Python and other open source libraries
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