author: @kawam tags:#ocr,#nlp,#ml
To scan and extract information from invoices and classify it into fields in a database, you can follow a two-step approach: OCR for text extraction and NLP for information extraction and classification. Here’s a recommended pipeline:
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Text extraction with OCR: Use an OCR library like Tesseract or a pre-trained OCR model like CRAFT to extract text from the scanned invoice images. Tesseract is widely used and has good accuracy, but you may need to preprocess the images (e.g., deskewing, binarization, and resizing) for better results.
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Information extraction and classification with NLP: Once you have the extracted text, use an NLP library to identify and classify the relevant fields, such as invoice number, date, supplier, total amount, etc. You can use a combination of rule-based methods, regex patterns, and machine learning techniques for this step.
Here’s a high-level workflow:
a. Preprocessing and cleaning the text: Clean the extracted text by removing unwanted characters, correcting common OCR errors, and normalizing the text.
b. Tokenization and parsing: Break the text into words or tokens and analyze the structure of the text using NLP libraries like SpaCy or NLTK.
c. Rule-based matching and regex patterns: Use regular expressions or rule-based matching to identify and extract specific fields such as dates, currency amounts, and invoice numbers. SpaCy’s Matcher or Python’s built-in re module can be helpful here.
d. Named entity recognition (NER) or text classification: Train an NLP model to recognize and classify specific fields in the invoice text. You can use pre-trained models from huggingface/transformers: 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. (github.com), fine-tune them on your data, or train custom models using SpaCy or TensorFlow.
- Storing data in the database: Once the information is extracted and classified, insert the data into the appropriate fields in your database.
To create an end-to-end solution, you can build a custom pipeline that combines these methods or use an existing OCR and data extraction platform like Rossum, Amazon Textract, or Google Cloud Vision API with Document AI, which are designed for document parsing and can handle invoices. These platforms often provide pre-trained models for common document types and can be fine-tuned to your specific use case. While these platforms might require subscriptions or API usage fees, they can save you time and effort in building and maintaining a custom solution.