PH Deck

Users need to manually extract text from scanned documents or images, which is time-consuming and error-prone, especially with handwriting or complex formats.

In the current situation, users struggle to extract text from images manually, which is time-consuming and prone to errors.

Users currently manually extract text from images or use basic OCR tools, leading to time-consuming processes and low accuracy, especially with complex layouts or multiple languages.

Users need to clean up text from Word, emails, or websites but manually remove HTML, emojis, symbols, or line breaks, leading to time-consuming and inconsistent formatting.

Users manually extract data from PDFs and images, leading to time-consuming processes and high error rates.

Users struggle with manually extracting and cleaning up tables from PDFs, especially those with complex layouts like invoices, bank statements, and reports. Manual extraction is time-consuming and error-prone, leading to inefficiency in data processing workflows.

Users manually extract emails from text, docs, and websites, leading to inefficiency, time consumption, and potential errors.

Users need to extract text from images, such as printed text, handwritten notes, or screenshots. The existing process can be cumbersome and often requires manual transcription, which is time-consuming and prone to errors. The drawbacks of the old situation include the difficulty to quickly and accurately find and extract desired text from various image formats and languages.

Users manually transcribe text from images or use external OCR tools, leading to time-consuming and error-prone processes.

Users need to manually identify and remove hidden AI fingerprints (invisible characters, smart quotes, em dashes) from ChatGPT-generated text, which is time-consuming, error-prone, and risks exposing AI origins.