From Chaos to Clarity: Parsing Grocery Receipts with PaddleOCR and AI

As I continued on my quest to parse the grocery receipt pictures I had taken over the years, I had previously tried Tesseract. While Tesseract is excellent for general-purpose OCR (Optical Character Recognition), I sensed that this task required something more specific—something trained specifically on receipt data.

I discovered a GitHub repository where the author has done extensive work on receipt parsing and provided some compelling examples. This was a great starting point and also inspired me with ideas on how to handle such unstructured data. The project utilizes PaddleOCR, which yielded impressive results in parsing the receipts. Some receipts were so poorly captured that I had no hope of extracting any text from them, yet it managed to salvage some data.

Another technique I learned was using Large Language Models (LLMs) to structure the data. However, I did not delve into vectorization, as I still need to grasp it better.
Directly using local LLMs did not yield satisfactory results. I then tried CGPT, which was significantly better due to its vast context window and broader world knowledge.

I wanted to conserve my API credits for this project, so I looked for a way to automate the browser version of ChatGPT. Fortunately, I found some code on GitHub which I could repurpose for this task. Despite these efforts, I realized I would still need to manually verify the results, as neither OCR nor parsing with LLMs is flawless.

One lesson for me is that, to effectively parse these receipts in the future, I need to ensure they are zoomed in, well-lit, and captured without any curves. There’s still a long way to go to obtain clean data from 100 receipts.

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