OCR (optical character recognition)

Single-Module Pipeline: `ocr`

This document is a walkthrough of how to assemble and use a single-module pipeline that only includes an ocr module.

Optical Character Recognition (OCR) is a technology that converts different types of documents, such as scanned paper documents, PDFs, and images captured by a digital camera, into editable and searchable data by extracting and organizing all text from the source document. OCR applications include digitizing printed texts for archiving, automating data entry processes, enabling text-to-speech for accessibility, and extracting information for machine learning and data analytics.

The document is divided into the following sections:

Pipeline Setup
Required Input Format
Using the Default Model
Using a Non-Default Model

Pipeline Setup

Let's first instantiate a single-module ocr pipeline.

We use the create_pipeline method for this, passing only the ocr module name into module_chain.

# create a pipeline with a single ocr module
pipeline = krixik.create_pipeline(name="single_ocr_1", module_chain=["ocr"])

Required Input Format

The ocr module accepts .png, .jpg, and .jpeg images as input.

Let's take a quick look at a valid input file, and then process it.

# examine the contents of a valid input file, this example taken from --> https://venngage.com/templates/menus/yellow-burger-takeout-menu-70bd168b-7813-44d4-9581-c7b6ab1c0f67
from IPython.display import Image

Image(filename=data_dir + "input/menu_1.png")

png

Using the Default Model

Let's process our test input file using the ocr module's default model: tesseract-en.

Given that this is the default model, we need not specify model selection through the optional modules argument in the process method.

# process the file with the default model
process_output = pipeline.process(
    local_file_path=data_dir + "input/menu_1.png",  # the initial local filepath where the input file is stored
    local_save_directory=data_dir + "output",  # the local directory that the output file will be saved to
    expire_time=60 * 30,  # process data will be deleted from the Krixik system in 30 minutes
    wait_for_process=True,  # wait for process to complete before returning IDE control to user
    verbose=False,
)  # do not display process update printouts upon running code

Part of the output of this process is printed below—for readability, text box coordinates have been excluded. To learn more about each component of the output, review documentation for the process method.

Because the output of this particular module-model pair is a JSON file, the process output is provided in this object as well (this is only the case for JSON outputs). Moreover, the output file itself has been saved to the location noted in the process_output_files key. The file_id of the processed input is used as a filename prefix for the output file.

print(process_output["process_output"][0]["text"])

culines

GOURMET VEGAN CUISINE

TAKEOUT MENU

BURGERS

Classic American Burger, $10.99
Hamburger, $9.99
Cheesy Burger, $8.50
Hawaiian Burger, $11

HOTDOGS

Classic HotDog, $6
Cheesy HotDog, $8.50
XL HotDog, $10.99

DRINKS

Coke, $2.99
Juice, $3.50
Coffee, $4.50

Order here: www.culines.com e 218-237-5684

To confirm that everything went as it should have, let's load in the text file output from process_output_files:

# load in process output from file - here we only print the text detected, and not the detection boxes, since that output is quite long
with open(process_output["process_output_files"][0]) as f:
    print(json.load(f)[0]["text"])

culines

GOURMET VEGAN CUISINE

TAKEOUT MENU

BURGERS

Classic American Burger, $10.99
Hamburger, $9.99
Cheesy Burger, $8.50
Hawaiian Burger, $11

HOTDOGS

Classic HotDog, $6
Cheesy HotDog, $8.50
XL HotDog, $10.99

DRINKS

Coke, $2.99
Juice, $3.50
Coffee, $4.50

Order here: www.culines.com e 218-237-5684

Using a Non-Default Model

To use a non-default model like tesseract-es, we must enter it explicitly through the modules argument when invoking the process method.

# process the file with a non-default model
process_output = pipeline.process(
    local_file_path=data_dir + "input/menu_1.png",  # all arguments but modules are the same as above
    local_save_directory=data_dir + "output",
    expire_time=60 * 30,
    wait_for_process=True,
    verbose=False,
    modules={"ocr": {"model": "tesseract-es"}},
)  # specify a non-default model for this process

The output of this process is printed below. Although the input image has English text instead of Spanish (the model's language), all English characters are in the Spanish alphabet, so it will work well for our purposes. An English-specific OCR model might not work as well for Spanish-language text in an image.