Hi, I'm using pytesseract to recognize the characters in the jpeg file for a scanned f

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Reading the characters in the image file with my own handwriting about handwriting-recognition HOT 1 OPEN

samkit-jain commented on September 26, 2024

Reading the characters in the image file with my own handwriting

from handwriting-recognition.

Comments (1)

samkit-jain commented on September 26, 2024

Hi @ozlem-atiz This repo is not designed for recognising continuous handwritten text (feel free to raise a PR that adds support for it). If your text is not continuous or if you are able to crop each character individually from the image as at https://github.com/samkit-jain/Handwriting-Recognition/blob/master/Screenshots/label_5.png, then you can run the model on those. The method

Handwriting-Recognition/Python/drawer.py

Lines 36 to 62 in 785cf3e

 def get_contours(self): 

 """ 

  Method to find contours in an image and crop them and return a list with cropped contours 

  """ 

 images = [] 

 main_image = self.img 

 orig_image = main_image.copy() 

 # convert to greyscale and apply Gaussian filtering 

 main_image = cv2.cvtColor(src=main_image, code=cv2.COLOR_BGR2GRAY) 

 main_image = cv2.GaussianBlur(src=main_image, ksize=(5, 5), sigmaX=0) 

 # threshold the image 

 _, main_image = cv2.threshold(src=main_image, thresh=127, maxval=255, type=cv2.THRESH_BINARY) 

 # find contours in the image 

 contours, _ = cv2.findContours(image=main_image.copy(), mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE) 

 # get rectangles containing each contour 

 bboxes = [cv2.boundingRect(array=contour) for contour in contours] 

 for bbox in bboxes: 

 x, y, width, height = bbox[:4] 

 images.append(orig_image[y:y + height, x:x + width]) 

 return images

is responsible for cropping the character bounding boxes from an image.

Handwriting-Recognition/Python/drawer.py

Lines 99 to 125 in 785cf3e

 @staticmethod 

 def convert_to_emnist(img): 

 """ 

  Method to make an image EMNIST format compatible. img is a cropped version of the character image. 

  Conversion process available in section II-A of the EMNIST paper available at https://arxiv.org/abs/1702.05373v1 

  """ 

 height, width = img.shape[:2] 

 # create a square frame with lengths equal to the largest dimension 

 emnist_image = np.zeros(shape=(max(height, width), max(height, width), 3), dtype=np.uint8) 

 # center the cropped image in it 

 offset_height = int(float(emnist_image.shape[0] / 2.0) - float(height / 2.0)) 

 offset_width = int(float(emnist_image.shape[1] / 2.0) - float(width / 2.0)) 

 emnist_image[offset_height:offset_height + height, offset_width:offset_width + width] = img 

 # resize to 26x26 using bi-cubic interpolation 

 emnist_image = cv2.resize(src=emnist_image, dsize=(26, 26), interpolation=cv2.INTER_CUBIC) 

 # refit the 26x26 to 28x28 so that characters don't touch the boundaries 

 fin_image = np.zeros(shape=(28, 28, 3), dtype=np.uint8) 

 fin_image[1:27, 1:27] = emnist_image 

 return fin_image

then converts them to the required EMNIST format and then you can pass in that to the model and get the prediction. Of course, you would have to make some changes and adjust or use the code accordingly.

from handwriting-recognition.

Reading the characters in the image file with my own handwriting about handwriting-recognition HOT 1 OPEN

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	def get_contours(self):
	"""
	Method to find contours in an image and crop them and return a list with cropped contours
	"""

	images = []
	main_image = self.img
	orig_image = main_image.copy()

	# convert to greyscale and apply Gaussian filtering
	main_image = cv2.cvtColor(src=main_image, code=cv2.COLOR_BGR2GRAY)
	main_image = cv2.GaussianBlur(src=main_image, ksize=(5, 5), sigmaX=0)

	# threshold the image
	_, main_image = cv2.threshold(src=main_image, thresh=127, maxval=255, type=cv2.THRESH_BINARY)

	# find contours in the image
	contours, _ = cv2.findContours(image=main_image.copy(), mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)

	# get rectangles containing each contour
	bboxes = [cv2.boundingRect(array=contour) for contour in contours]

	for bbox in bboxes:
	x, y, width, height = bbox[:4]
	images.append(orig_image[y:y + height, x:x + width])

	return images

	@staticmethod
	def convert_to_emnist(img):
	"""
	Method to make an image EMNIST format compatible. img is a cropped version of the character image.

	Conversion process available in section II-A of the EMNIST paper available at https://arxiv.org/abs/1702.05373v1
	"""

	height, width = img.shape[:2]

	# create a square frame with lengths equal to the largest dimension
	emnist_image = np.zeros(shape=(max(height, width), max(height, width), 3), dtype=np.uint8)

	# center the cropped image in it
	offset_height = int(float(emnist_image.shape[0] / 2.0) - float(height / 2.0))
	offset_width = int(float(emnist_image.shape[1] / 2.0) - float(width / 2.0))

	emnist_image[offset_height:offset_height + height, offset_width:offset_width + width] = img

	# resize to 26x26 using bi-cubic interpolation
	emnist_image = cv2.resize(src=emnist_image, dsize=(26, 26), interpolation=cv2.INTER_CUBIC)

	# refit the 26x26 to 28x28 so that characters don't touch the boundaries
	fin_image = np.zeros(shape=(28, 28, 3), dtype=np.uint8)
	fin_image[1:27, 1:27] = emnist_image

	return fin_image