trocr-base-handwritten-hist-swe-2

Model · Image to text

nougat-base

AI at Meta

Nougat model trained on PDF-to-markdown. It was introduced in the paper Nougat: Neural Optical Understanding for Academic Documents by Blecher et al. and first released in this repository. Disclaimer: The team releasing Nougat did not write a model card for this model so this model card has been written by the Hugging Face team. Note: this model corresponds to the "0.1.0-base" version of the original repository. Nougat is a Donut model trained to transcribe scientific PDFs into an easy-to-use markdown format. The model consists of a Swin Transformer as vision encoder, and an mBART model as text decoder. The model is trained to autoregressively predict the markdown given only the pixels of…

Model · Image to text

trocr-base-printed

Microsoft

TrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. Disclaimer: The team releasing TrOCR did not write a model card for this model so this model card has been written by the Hugging Face team. The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which…

Model · Image to text

trocr-base-handwritten

Microsoft

TrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. Disclaimer: The team releasing TrOCR did not write a model card for this model so this model card has been written by the Hugging Face team. The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which…

Model · Image to text

blip-image-captioning-large

Salesforce AI Research

captioning pretrained on COCO dataset - base architecture (with ViT large backbone). Authors from the paper write in the abstract: Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the…

Model · Image to text

pix2struct-base

Google

This model is the pretrained version of Pix2Struct, use this model for fine-tuning purposes only. Pix2Struct is an image encoder - text decoder model that is trained on image-text pairs for various tasks, including image captionning and visual question answering. The full list of available models can be found on the Table 1 of the paper: The abstract of the model states that: forms. Perhaps due to this diversity, previous work has typically relied on domainspecific recipes with limited sharing of the underlying data, model architectures, and objectives. We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding, which can be finetuned on tasks…

Model · Image to text

donut-boletas-peru

André Edgardo Saras Rivera

Modelo experimental de extracción estructurada, sin OCR explícito, para imágenes de boletas peruanas. Es una adaptación de bajo recurso de naver-clova-ix/donut-base-finetuned-cord-v2, no un modelo entrenado desde cero. El modelo recibe una imagen completa y genera una representación que DonutProcessor.token2json convierte al siguiente esquema: Este checkpoint está pensado para investigación, demostraciones y prototipos supervisados sobre boletas impresas peruanas en español. Puede servir como punto de partida para nuevas evaluaciones o fine-tuning con datos propios. No debe utilizarse sin revisión humana para contabilidad, tributación, auditoría, pagos, cumplimiento normativo ni otras…