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| from transformers import ViTFeatureExtractor, BertTokenizer, VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor | |
| import gradio as gr | |
| title="Multilingual OCR (currently recognises: English and Chinese)" | |
| description="m_OCR(multilingual OCR) is a Vision-Encoder-Decoder model (based on the concept of TrOCR) which uses pre-trained facebook's vit-mae-large as the encoder and xlm-roberta-base as the decoder. \nIt has been trained on IAM, SROIE 2019, text_renderer Chinese (synthetic) and TRDG (synthetic) datasets (amounting to approx 1.4 Million samples) for English and Chinese document text-recognition." | |
| examples =[["demo_image/img1.png"], ["demo_image/img2.jpeg"], ["demo_image/img3.jpeg"], ["demo_image/img4.jpeg"], ["demo_image/img5.jpeg"], ["demo_image/img6.jpeg"]] | |
| model=VisionEncoderDecoderModel.from_pretrained("priyank-m/m_OCR") | |
| tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base") | |
| feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-mae-large") | |
| def run_ocr(image): | |
| pixel_values = feature_extractor(image, return_tensors="pt").pixel_values | |
| # autoregressively generate caption (uses greedy decoding by default ) | |
| generated_ids = model.generate(pixel_values, max_new_tokens=50) | |
| generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return generated_text | |
| demo = gr.Interface(fn=run_ocr, inputs="image", outputs="text", title=title, description=description, examples=examples) | |
| demo.launch() |