OCR | Optical Character Recognition in Chinese Letter Manuscripts | Computer Vision library

I have to parse lists of names, addresses, etc. that were OCRed and have invalid/incorrect characters in them and on the state postal code I need to recognize the pattern with a 2 character state followed by a 5 digit postal code and replace any non numeric characters in the postal code. I might have OK 7-41.03 at the end of a string I need to remove the hyphen and period. I know that re.sub('[^0-9]+', '', '7-41.03') will remove the desired characters but I need it only replace characters in numbers when found at the end of the string and only if preceded by a two character state wrapped in spaces like OK. It seems if I add anything to the regular expression as far as a lookbehind expression then I can't seem to get the characters replaced. I've come up with the following but I think there must be a simpler expression to accomplish this. Example:

Is it possible to use Google ML-Kit On-Device Text Recognition in Flutter? All of the tutorials and resources I am finding online are all firebase_ml_vision, but I am looking for one that uses the no-cost OCR from Google ML-Kit. How would I do this in Flutter?

EDIT: SOLVED - when I posted this the package was not there, but now it is.

As I'm working on a script to correct formatting errors from documents produced by OCR, I ran into an issue where, depending on which loop I run first, my program runs about 80% slower.

Here is a simplified version of my code. I have the following loop to check for uppercase errors (e.g., "posSible"):

so first of this is my first time asking a question here so forgive me if I make any mistakes. My Problem is as follows: I'm using python to sort through a bunch of images. The images are sorted by many criteria, one of which is the text inside the Image. I've got OCR working and have a list of "bad" words which arent supposed to be in the Image. The problem is that the OCR often confuses some letters, for example e and a. The question is if there is an easy way to generate similar looking words. Like create_similar("test") And output would be ["test", "tast" "lest"] and so on. So I could use that as the list of Bad words and avoid false negatives. If I'm just missing a really obvious solution, please tell me. I've been trying for hours now and just can't get it to work.

I am working on a python program that reads license plates from trucks. An image that gets processed by this program and filters the characters as output. Here is the input of the image in the program:

I am trying to extract Hindi text from a PDF. I tried all the methods to exract from the PDF, but none of them worked. There are explanations why it doesn't work, but no answers as such. So, I decided to convert the PDF to an image, and then use pytesseract to extract texts. I have downloaded the Hindi trained data, however that also gives highly inaccurate text.

That's the actual Hindi text from the PDF (download link):

That's my code so far:

I have some sketched images where the images contain text captions. I am trying to remove those caption.

I am using this code:

I have the following code, which OCR's all PDF files in a specific folder (d:\extracttmp2), but it does not rename the files as I would like, or put the new files in the right place.

Currently, all files are within subfolders of 'extracttmp2'.

The OCR runs correctly, but I would like the OCR'ed files to be renamed to: -_ocred.pdf. Naming them in such a manner will produce no file overwrites.

Currently, the code OCR's the files, but it saves the new files to the folder above the folder they are located in. It also saves the filenames as "JAN_ocred.pdf", for example, for a file named "JAN.pdf". The result of saving up one folder leads to some file overwrites, which is unwanted.

Also, it doesn't matter if the OCR'ed files remain in the folder where the un-OCR'ed files are located, or if they're saved up one folder. The desired renaming will eliminate any overwrites.

The software I'm using is PDF24. https://creator.pdf24.org/manual/10/#command-line. However, I think that my problem is not with the OCR software, but my syntax in the batch script.

Can anyone tell me what I am doing wrong?

Link to original image https://ibb.co/0VC6vkX

I am currently working with an OCR Project. I pre-processed the image, and then applied pre-trained EAST model for text detection.