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What languages does Tesseract support?

What languages does Tesseract support?

The initial versions of Tesseract could only recognize English-language text. Tesseract v2 added six additional Western languages (French, Italian, German, Spanish, Brazilian Portuguese, Dutch).

How do I add languages to Tesseract?

To install other languages, download the respective language pack ( . traineddata file) from https://github.com/tesseract-ocr/tessdata/ and place it in C:\\Program Files\\Tesseract-OCR\\tessdata (or wherever Tesseract OCR is installed).

What is multilingual OCR?

Abstract: In Indian scenario, a document analysis system has to support multiple languages at the same time. This demands development of a multilingual OCR system which can work seamlessly across Indic scripts. …

How do you run Tesseract on Google Colab?

Here are the steps to extract text from the image in Google Colab Notebook for OCR using Pytesseract:

  1. Step1. Install Pytesseract and tesseract-OCR in Google Colab. !
  2. Step2. import libraries.
  3. Step3. Upload Image to the Colab.
  4. Step4. Text Extraction.
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How do you speed up Tesseract?

To speed up the process, one should make a list of image paths and feed it to tesseract. Using SSDs or RAM as Disk : If there are large number of images, it can help in saving lot of I/O time. SSDs will have faster access and loading time.

How do I use OCR in Foxit Phantom?

To OCR a PDF document using Foxit Reader simply follow these steps:

  1. Step 1: Load your PDF File. Click the ‘Home’ button and then select ‘Convert’.
  2. Step 2: Select the Output Options. Simply select ‘Editable Text’ and this will make the PDF text editable.
  3. Step 3: Complete the OCR Process.

How do you train OCR models?

Building your own Attention OCR model

  1. Gather annotated training data.
  2. Get crops for each frame of each video where the number plates are.
  3. Generate tfrecords for all the cropped files.
  4. Place them in models/research/attention_ocr/python/datasets as required (in the FSNS dataset format).
  5. Train the model using Attention OCR.