easyocr.recognize is significantly slower when given several boxes to estimate, rather than running it several time with one box each time

[fxmarty]

Hello,

Thank you for this tool, it is great. I want to build on top of it, and execution time is a matter of importance for me (even on CPU).

I don't know if it's a bug or a feature, but I've noticed that easyocr.recognize is significantly slower when called once and given n boxes to estimate the text in, rather than called n times with one box each time.

How to reproduce

1/ Download

Run

import easyocr
reader = Reader(['ja'], gpu = False)
image = "path/to/example.png"

result = reader.detect(image)
print(result)
# returns ([[117, 471, -3, 37], [120, 596, 32, 64]], [])

Then run

import time

L = result[0]

start_time = time.time()
estimate = reader.recognize(filename,
                            horizontal_list=L,
                            free_list=[]
                            )
print(estimate)
print("--- %s seconds ---" % (time.time() - start_time))

and

start_time = time.time()
for box in L:
    estimate = reader.recognize(filename,
                                horizontal_list=[box],
                                free_list=[]
                                )
    print(estimate)
print("--- %s seconds ---" % (time.time() - start_time))

For me the first one takes ~3.45s, and the second one takes ~2.95s, i.e. about 15% faster.

Is it an expected behavior?

Many thanks!

[rkcosmos]

It should not be significantly slower. Can you try with other cases to see if this behavior persist? (especially with a lot of boxes)

[fxmarty]

Thank you very much for your answer!

I tried with the following image:

Note that I run (for now) on CPU so I am very slow.

Here is my script

from easyocr import Reader
import time
import numpy as np

reader = Reader(['ja'], gpu = False)
image = "/path/to/image.png"

result = reader.detect(image)
print(result)

L = result[0]

tps = []
for i in range(10):
    start_time = time.time()
    estimate = reader.recognize(image,
                                horizontal_list=L,
                                free_list=[]
                                )
    print("--- %s seconds ---" % (time.time() - start_time))
    tps.append(time.time() - start_time)
print("Mean time (10 runs) all at once:", np.mean(tps), "seconds")

tps = []
for i in range(10):
    start_time = time.time()
    for box in L:
        estimate = reader.recognize(image,
                                    horizontal_list=[box],
                                    free_list=[]
                                    )
    print("--- %s seconds ---" % (time.time() - start_time))
    tps.append(time.time() - start_time)
print("Mean time (10 runs) one by one:", np.mean(tps), "seconds")

Output:

Using CPU. Note: This module is much faster with a GPU.

([[178, 531, -3, 35], [181, 513, 27, 67], [231, 361, 59, 97], [233, 479, 89, 131], [229, 480, 116, 164], [231, 417, 155, 193], [183, 249, 185, 223], [259, 531, 185, 223], [183, 279, 215, 253], [258, 448, 243, 283], [247, 465, 276, 312], [247, 463, 308, 344], [233, 477, 338, 376], [233, 419, 371, 409], [231, 482, 395, 443], [218, 348, 429, 474], [219, 493, 465, 503], [227, 487, 495, 533], [275, 337, 529, 547], [191, 525, 539, 581], [191, 409, 572, 612], [324, 390, 606, 638], [211, 501, 627, 671], [325, 387, 661, 697]], [])

--- 33.16319298744202 seconds ---
--- 30.86881136894226 seconds ---
--- 32.36347842216492 seconds ---
--- 43.46124005317688 seconds ---
--- 31.177638292312622 seconds ---
--- 41.59009075164795 seconds ---
--- 29.97151231765747 seconds ---
--- 34.05026316642761 seconds ---
--- 34.657999992370605 seconds ---
--- 33.71805453300476 seconds ---
Mean time (10 runs) all at once: 34.50231809616089 seconds

--- 16.246243715286255 seconds ---
--- 19.049020528793335 seconds ---
--- 27.189735889434814 seconds ---
--- 20.718939065933228 seconds ---
--- 20.56512212753296 seconds ---
--- 17.336503267288208 seconds ---
--- 16.20930004119873 seconds ---
--- 19.736635446548462 seconds ---
--- 23.933078050613403 seconds ---
--- 24.421704053878784 seconds ---
Mean time (10 runs) one by one: 20.540727972984314 seconds

I didn't specifically check CPU usage during the two runs, but at least on my side I had nothing new / different running between the two. I wonder if you can reproduce it or if it comes from me.

Thanks again!

[rkcosmos]

Thanks for pointing this out. I can reproduce this undesirable behavior. I'll look into the cause of this problem.

[rkcosmos]

Last update should fix this. It should now a little bit faster (<10%) to process n boxes comparing to manual looping. Please install the last code (pip install --upgrade git+git://github.com/jaidedai/easyocr.git) and confirm if it fix your problem.