typo v6

This model is designed for recognizing upright handwritten English text from scanned historical and literary documents. The training data includes pages containing cursive handwriting, punctuation marks, symbols, mixed sentence structures, and varying line lengths. The model was trained using manually corrected transcriptions in Transkribus to improve recognition accuracy for challenging handwritten text styles. The dataset contains pages with natural variations in writing flow, including commas, periods, semicolons, quotation marks, apostrophes, and irregular spacing. Multiple corrected pages were used to help the model learn consistent character patterns, word structures, and symbol recognition across different contexts. The training process focused on improving recognition of upright handwritten typography while maintaining readability and preserving the original textual structure. This model is especially useful for: * Handwritten English manuscripts * Historical or literary documents * Upright cursive writing * OCR transcription workflows * Digital archiving projects * Research and text digitization The model performs best on clear, upright page images with readable handwriting and consistent scan quality. It may still produce errors on heavily damaged pages, extremely decorative handwriting, low-resolution scans, or unusual symbols not sufficiently represented in the training data. The goal of this project is to create a specialized OCR model optimized for upright handwritten English text recognition while continuously improving accuracy through additional corrected training pages and iterative retraining.