microdocs

Inspiration

Existing systems are generic and fails at customization

What it does

MicroDocs introduces a set of plugins for document management systems enhanced with AI to correct OCR errors and summarize documents, boosting productivity. This plan aims to explain our solution and outline growth strategies, demonstrating our potential in the creation of customized intelligent document solutions.

How we built it

Our OCR correction and summarization features leverage state-of-the-art AI technologies to deliver accuracy and efficiency in document management. OCR-generated text, allowing the agent to learn from examples and identify potential mistakes and errors.

Common OCR errors such as under-segmentation and spelling mistakes can often be rectified using existing grammatical correctors. However, where our agent truly excels is in addressing errors specific to different types of documents. By understanding the nuances and intricacies of various document formats, our agent can accurately identify and correct errors that may arise during the OCR process, ensuring the utmost accuracy and reliability of the extracted text.

Summarization:

For document summarization, we utilize a BERT-based language model capable of supporting three main languages commonly used in Algeria: Arabic, French, and English. BERT (Bidirectional Encoder Representations from Transformers) is a powerful natural language processing (NLP) model known for its ability to understand context and generate high-quality summaries of textual content.

By leveraging BERT's advanced language understanding capabilities, our summarization feature can effectively analyze and condense lengthy documents into concise summaries, highlighting key information and insights. Whether it's extracting critical points from legal documents, financial reports,etc.. Our summarization technology enables users to quickly grasp the essential content of any document, saving time and improving productivity.

OCR Correction:

Our OCR correction technology is powered by an intelligent agent trained in a supervised manner using a diverse dataset of bills. This dataset comprises pairs of correct text and