# Image to Text

This is an image captioning model based on ViT and GPT2 architectures, capable of generating natural language descriptions for input images.

Bpe-vocab-n-OCR is an advanced text extraction tool based on OCR, optimized for generating structured and tokenized output.

An image-to-text model based on the transformers library, supporting the conversion of image content into descriptive text.

A specialized image-to-text model for the financial domain, supporting English text recognition, primarily used for processing image content in financial documents.

TrOCR is a Transformer-based optical character recognition model designed to extract text content from images.

This model is a fine-tuned version of Microsoft's TrOCR printed text model, specifically designed for Sinhala OCR recognition tasks.

An optical character recognition model based on Hugging Face Transformers, specifically designed for recognizing MNIST-style digit images

A captcha recognition model fine-tuned based on Microsoft's trocr-base-printed model, specifically designed for OCR tasks involving printed text

This is an image captioning model based on Vision Transformer (ViT) and GPT2 architectures, capable of generating natural language descriptions for input images.

This is a Transformer-based OCR model specifically designed for recognizing Persian text in images.

Optical Character Recognition model specialized for Japanese text in manga

A model fine-tuned on an image folder dataset based on naver-clova-ix/donut-base, with no specific use case explicitly stated

An OCR model for Hebrew image-to-text conversion, capable of recognizing Hebrew text in images.

Pix2Struct is an image encoder-text decoder model trained on image-text pairs, supporting various tasks including image captioning and visual question answering.

This model is a fine-tuned version of naver-clova-ix/donut-base on an image folder dataset, suitable for document understanding tasks.

This is a proof-of-concept model for Korean text recognition, trained on the TrOCR architecture, supporting Korean text extraction from images.

A document understanding model fine-tuned based on philschmid/donut-base-sroie

A fine-tuned Donut model for extracting text information from handwritten medical prescription images.

A document understanding model fine-tuned from naver-clova-ix/donut-base, suitable for image text extraction tasks

A branch model based on microsoft/trocr-base-printed, specializing in OCR tasks for printed text.

An optical character recognition (OCR) model based on TensorFlow 2 and PyTorch, supporting multilingual text detection and recognition

This is an image captioning model based on ViT and GPT2 architectures, capable of generating natural language descriptions for input images.

TrOCR is a Transformer-based pretrained optical character recognition model developed by Microsoft, suitable for single-line text image OCR tasks.

This is an image-to-text generation model capable of receiving images and outputting descriptive text.

TrOCR is a Transformer-based pre-trained optical character recognition model that adopts an encoder-decoder architecture, suitable for OCR tasks on single-line text images.