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image = cv.imread("/content/Demo2.png")
def preprocess_image(image):
gray_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
ret, bin_image = cv.threshold(gray_image, 127, 255, cv.THRESH_OTSU)
bin_image = cv.copyMakeBorder(bin_image, int(0.10 * image.shape[0]), int(0.05 * image.shape[0]), int(0.05 * image.shape[1]), int(0.10 * image.shape[1]), cv.BORDER_CONSTANT, value=(255, 255, 255))
return bin_image
bin_image = preprocess_image(image)
def split_image_into_lines(image):
lines = []
while (image.shape[0] > 20):
flag1 = 0
flag2 = 0
for i in range(image.shape[0]):
if flag1 == 0:
for j in range(image.shape[1]):
pixel_value = image[i][j]
if (pixel_value == 0) & (flag1 == 0):
start = i
flag1 = 1
flag2 = 1
if flag2 == 1:
num_white_pixels = np.sum(image[i + 1] == 255)
if (num_white_pixels > 0.98 * image.shape[1]):
end = i + 1
break
line = image[int(start - 0.2 * (end - start + 1)): int(end + 1 + 0.2 * (end - start + 1))][:]
if line.shape[0] > 20:
line_rgb = cv.cvtColor(line, cv.COLOR_GRAY2RGB)
lines.append(line_rgb)
pads = 255 * np.ones((20, image.shape[1]), dtype='uint8')
new_image = image[int(end + 2 -(0.2 * (end - start + 1))):][:]
new_image = np.concatenate((pads, new_image))
image = new_image
return lines
lines = split_image_into_lines(bin_image)
def generate_text(line):
pixel_values = processor(images=line, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values, max_new_tokens=50)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text
with ProcessPoolExecutor() as executor:
results = ' '.join(executor.map(generate_text, lines))
#improve results with llm
client = OpenAI()
completion = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": f"I have a string that was extracted from an image of handwritten text. The extraction process introduced minor grammatical, spelling, and punctuation errors. Please carefully review the text below and make any necessary corrections to improve readability and accuracy while preserving the original meaning. Do not change the content or style beyond necessary corrections. Return the corrected text only without adding any headings, explanations, or extra formatting. Text: {results}"
}
]
)
improved_text = completion.choices[0].message.content
def put_text(text, font, font_scale, color, thickness, max_width, out_image_width, top_margin):
words = text.split(" ")
lines = []
current_line = ""
for word in words:
if cv.getTextSize(current_line + " " + word, font, font_scale, thickness)[0][0] <= (max_width * out_image_width):
current_line += " " + word
else:
lines.append(current_line)
current_line = word
lines.append(current_line)
out_image_height = sum([cv.getTextSize(line, font, font_scale, thickness)[0][1] for line in lines]) + 2 * top_margin + 20 * (len(lines) - 1) #20 is the gap between two consecutive lines
out_image = 255 * (np.ones((out_image_height, out_image_width, 3), dtype=np.uint8))
top = top_margin
for line in lines:
cv.putText(out_image, line.strip(), (int(((1 - max_width) * out_image_width) / 2), top), font, font_scale, 0, thickness, lineType=cv.LINE_AA)
top += cv.getTextSize(line.strip(), font, font_scale, thickness)[0][1] + 20
return out_image
font = cv.FONT_HERSHEY_DUPLEX
font_scale = 2
color = 0
thickness = 2
max_width = 0.9
out_image_width = 1500
top_margin = 100
out_image = put_text(improved_text, font, font_scale, color, thickness, max_width, out_image_width, top_margin) |