README.md

eDOCr

eDOCr is a packaged version of keras-ocr that facilitates end-to-end digitization of mechanical EDs. Developed for Windows OS and using Python as the primary programming language. The implementation is discussed in the researh paper Optical character recognition on engineering drawings to achieve automation in production quality control

Getting Started

Installation

The test environment I used is Python = 3.9 and TensorFlow == 2.13.0.

conda create -n edocr python=3.9 -y 
conda activate edocr

# To install from Source
cd path/to/your/folder
git clone https://github.com/yyyzzx7/eDOCr.git
cd eDOCr
pip install -r requirements.txt
pip install .

Using

There are two ways of using eDOCr: from terminal and from your own python file.

From Terminal:

python PATH/TO/YOUR/FOLDER/eDOCr/ocr_it.py PATH/TO/YOUR/DRAWING/my_drawing.pdf

Exp: (Note that the drawing.pdf is in the root folder)

python eDOCr/ocr_it.py drawing.pdf

From your own python file

More customization is possible using your own python file, such as selecting a different model, alphabet or changing colors.

demo.py:

# Importing packages
import os
from eDOCr import tools
import cv2
import string
from skimage import io

# Loading image and destination file
dest_DIR = './Results'
file_path = './drawing.png'
filename = os.path.splitext(os.path.basename(file_path))[0]
img = cv2.imread(file_path)

# Selecting alphabet and model (Note that alphabet and alphabet model need to match)
GDT_symbols = '⏤⏥○⌭⌒⌓⏊∠⫽⌯⌖◎↗⌰'
FCF_symbols = 'ⒺⒻⓁⓂⓅⓈⓉⓊ'
Extra = '(),.+-±:/°"⌀'

alphabet_dimensions = string.digits + 'AaBCDRGHhMmnx' + Extra
model_dimensions = 'eDOCr/keras_ocr_models/models/recognizer_dimensions.h5'
alphabet_infoblock = string.digits+string.ascii_letters+',.:-/'
model_infoblock = 'eDOCr/keras_ocr_models/models/recognizer_infoblock.h5'
alphabet_gdts = string.digits + ',.⌀ABCD' + GDT_symbols
model_gdts = 'eDOCr/keras_ocr_models/models/recognizer_gdts.h5'

# Selecting personalized color palette and cluster setting
color_palette = {'infoblock': (180, 220, 250), 'gdts': (94, 204, 243), 'dimensions': (93, 206, 175), 'frame': (167, 234, 82), 'flag': (241, 65, 36)}
cluster_t = 20

# eDOCr functions
class_list, img_boxes = tools.box_tree.findrect(img)
boxes_infoblock, gdt_boxes, cl_frame, process_img = tools.img_process.process_rect(class_list, img)
io.imsave(os.path.join(dest_DIR, filename + '_process.jpg'), process_img)

infoblock_dict = tools.pipeline_infoblock.read_infoblocks(boxes_infoblock, img, alphabet_infoblock, model_infoblock)
gdt_dict = tools.pipeline_gdts.read_gdtbox1(gdt_boxes, alphabet_gdts, model_gdts, alphabet_dimensions, model_dimensions)
 
process_img = os.path.join(dest_DIR, filename + '_process.jpg')

dimension_dict = tools.pipeline_dimensions.read_dimensions(process_img, alphabet_dimensions, model_dimensions, cluster_t)
mask_img = tools.output.mask_the_drawing(img, infoblock_dict, gdt_dict, dimension_dict, cl_frame, color_palette)

# Record the results
io.imsave(os.path.join(dest_DIR, filename + '_boxes.jpg'), img_boxes)
io.imsave(os.path.join(dest_DIR, filename + '_mask.jpg'), mask_img)
tools.output.record_data(dest_DIR, filename, infoblock_dict, gdt_dict, dimension_dict)