https://github.com/oleehyo/texteller
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Handwritten Text Recognition (HTR) is the task of automatically identifying and transcribing handwritten text from images or scanned documents into machine-readable text. The goal is to develop a system capable of accurately interpreting diverse handwriting styles, accounting for variations in alignment, stroke, spacing, and noise. This task involves detecting handwritten regions within an image, extracting the text content, and converting it into a structured digital format, enabling further search, indexing, or data analysis.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
Digital Peter: Dataset, Competition and Handwriting Recognition Methods
Full Page Handwriting Recognition via Image to Sequence Extraction
Full Page Handwriting Recognition via Image to Sequence Extraction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.