Mychen76

Models by this creator

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mistral7b_ocr_to_json_v1

mychen76

Total Score

54

The mistral7b_ocr_to_json_v1 is a fine-tuned Large Language Model (LLM) developed by mychen76 that specializes in converting OCR text to a well-formed JSON object. It is based on the Mistral-7B-v0.1 model, which outperforms Llama 2 13B on various benchmarks. The model aims to leverage the strengths of both OCR engines and LLM models to streamline tasks like invoice or receipt image to JSON object conversion. Similar models include Mixtral-8x7B-Instruct-v0.1, llava-v1.6-mistral-7b-hf, Mistral-7B-Instruct-v0.1, and Mixtral-8x7B-v0.1, all of which leverage the Mistral model architecture. Model inputs and outputs Inputs OCR text boxes**: The model takes the output of an OCR engine, which is a list of text boxes with bounding box coordinates, as input. Outputs Structured JSON object**: The model generates a well-formed JSON object representing the receipt or invoice data, based on the provided OCR text. Capabilities The mistral7b_ocr_to_json_v1 model is capable of converting unstructured OCR text into a structured JSON representation. This can be useful for automating various document processing tasks, such as invoice or receipt digitization, where the goal is to extract key information like item names, quantities, prices, and taxes into a machine-readable format. What can I use it for? This model can be employed in a variety of document processing and automation scenarios. For example, you could use it to build an application that automatically extracts data from images of receipts or invoices and transforms them into a structured format for further processing or storage. This could be particularly useful for businesses that deal with a high volume of physical documents, as it can streamline their data entry and record-keeping processes. Things to try One interesting thing to try with this model is to experiment with different OCR engines and evaluate how the quality of the input text affects the performance of the JSON generation. You could also try incorporating additional context, such as the document type or the business domain, to see if that improves the accuracy of the generated JSON output.

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Updated 5/17/2024