3/8/2023 0 Comments Kami ocr toolThe output that you would receive after running the above script is “MASKAY”. The model is meant for text recognition so you should not expect it to detect the text in the image. Unlike the other two, the models only output the final text output without the text location. # installation pip install transformers # import from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image # inference model_version = "microsoft/trocr-base-printed" processor = om_pretrained(model_version) model = om_pretrained(model_version) image = Image.open(img_path).convert("RGB") pixel_values = processor(image, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) The code has been included in the famous Huggingface library so we can use the trained model directly from the library. It is developed based on the image Transformer encoder and an autoregressive text decoder (Similar to GPT-2). TrOCR was initially proposed in TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Minghao Li, Tengchao Lv, Lei Cui and etc. Let’s use the same image above to examine the model performance: Here is how you can use it: # installation pip install paddleocr paddlepaddle # import from paddleocr import PaddleOCR # inference ocr = PaddleOCR(use_angle_cls=True, lang='en') result = ocr.ocr(img_path, cls=True) You can fine-tune the model on your dataset with the provided script.As data is important to train the OCR model, they also have a tool called Style-text for you to quickly synthesize your image so that you have more images to train your model, making it robust to use in the production environment. For example, they provide the PPOCRLabel for you to quickly label the text in the image. They have multiple tools to support you for data labeling.They support multiple languages such as Chinese, English, Korean, Japanese, German and etc.They also provide an extremely lightweight and yet powerful model called PP-OCRv2 so that you don’t need to worry about a large memory problem. You can use their existing models for your applications.Here is a number of things that you can do with the open-source code: This makes it one of the most powerful open-source OCR software. The models used in the framework were trained using State-Of-The-Art (SOTA) techniques (such as CML knowledge distillation and CopyPaste data expansion strategy) and with tons of printed and handwritten images. I have been using this software tool for quite a while and I am really amazed by how much the team has done to make this free product as powerful as any commercial OCR software in the market. PaddleOCR is an open-source product developed by the Baidu team in China. As a bonus, I will also include scripts that will allow you to experience all the models at once. The focus of this article will only be on tools that use deep learning models. OCR can be done using either traditional computer vision techniques or more advanced deep learning techniques. The app offers users the convenience of scanning questions they have on paper and having them translated into machine-readable format through scanning. As one example, Photomath, a startup that breaks down problems into simple steps to help people understand mathematics. Among Edtech startups, there are some who heavily rely on OCR.To save the forms in the cloud, you need OCR software, which converts the text into machine-readable files. In the property industry, home buyers and agents typically fill out their agreement forms in paper form.For this, the documents must be scanned and converted into a machine-readable format. To digitalize their operations, banks need to store all paperwork into a cloud database.There are a number of cases we need to detect text in an image. Image from Part I - 5 open-source tools you can use to train your own data and deploy it for your next OCR project! Part II - From labelling to serving your OCR model! (Coming soon)
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