112 lines
3.9 KiB
Markdown
112 lines
3.9 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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- zh
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base_model:
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- prithivMLmods/Qwen2-VL-OCR-2B-Instruct
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pipeline_tag: image-to-text
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library_name: transformers
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tags:
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- text-generation-inference
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- bpe
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- ocr
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---
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# **Bpe-vocab-n-OCR**
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**Bpe-vocab-n-OCR** is an advanced OCR-based text extraction tool optimized for generating structured, tokenized outputs. Built upon a powerful vision-language architecture with enhanced OCR and multilingual support, Bpe-vocab-n-OCR accurately extracts text from images and returns it as a comma-separated sequence.
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#### Key Enhancements:
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* **Advanced OCR Engine**: Fine-tuned on extensive datasets, Bpe-vocab-n-OCR ensures precise text recognition and tokenization.
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* **Optimized for Tokenized Output**: Produces structured comma-separated text, making it ideal for downstream NLP tasks, automation pipelines, and database integrations.
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* **Enhanced Multilingual OCR**: Supports text extraction in multiple languages, including English, Chinese, Japanese, Korean, Arabic, and more.
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* **Multimodal Processing**: Seamlessly processes both image and text inputs, providing structured tokenized outputs.
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* **Secure and Optimized Model Weights**: Employs safetensors for efficient and secure model loading.
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### How to Use
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```python
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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# Load the Bpe-vocab-n-OCR model with optimized parameters
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"prithivMLmods/Tokenized-OCR", torch_dtype="auto", device_map="auto"
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)
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# Recommended acceleration for performance optimization:
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# model = Qwen2VLForConditionalGeneration.from_pretrained(
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# "prithivMLmods/Tokenized-OCR",
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# torch_dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2",
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# device_map="auto",
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# )
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# Load the default processor for Bpe-vocab-n-OCR
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processor = AutoProcessor.from_pretrained("prithivMLmods/Tokenized-OCR")
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# Define the input messages with both an image and a text prompt
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://flux-generated.com/sample_image.jpeg",
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},
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{"type": "text", "text": "Extract and return the tokenized OCR text from the image, ensuring each word is accurately recognized and separated by commas."},
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],
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}
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]
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# Prepare the input for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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# Generate the output
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generated_ids = model.generate(**inputs, max_new_tokens=256)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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### **Key Features**
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1. **High-Accuracy OCR Processing**
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* Extracts and tokenizes text from images with exceptional precision.
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2. **Multilingual Text Recognition**
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* Supports multiple languages, ensuring comprehensive OCR capabilities.
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3. **Comma-Separated Tokenized Output**
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* Generates structured text for seamless NLP and data processing tasks.
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4. **Efficient Image & Text Processing**
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* Handles both visual and textual inputs, ensuring accurate OCR-based extraction.
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5. **Optimized for Secure Deployment**
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* Uses safetensors for enhanced security and model efficiency. |