Transformer Based Ocr

Transformer-based Optical Character Recognition (OCR) leverages the power of transformer neural networks to achieve highly accurate text extraction from images. Current research focuses on improving model adaptability to diverse languages and complex scenarios, such as mixed text types (handwritten, printed, etc.) and challenging layouts, often employing techniques like transfer learning and parameter-efficient fine-tuning within the TrOCR architecture. These advancements significantly enhance OCR performance, particularly for resource-constrained languages and applications involving historical documents or visually complex scenes, leading to improved accuracy and efficiency in document digitization and text analysis.