add example images

README.md

@@ -47,11 +47,11 @@ Note: for Windows, please install torch and torchvision first by following offic
 
 ``` python
 import easyocr
-reader = easyocr.Reader(['th','en'])
-reader.readtext('test.jpg')
+reader = easyocr.Reader(['ch_tra','en'])
+reader.readtext('chinese.jpg')
 ```
 
-Note: Instead of filepath 'test.jpg', you can also pass OpenCV image object or image file as bytes.
+Note: Instead of filepath 'chinese.jpg', you can also pass OpenCV image object (numpy array) or image file as bytes.
 
 Model weight for chosen language will be automatically downloaded or you can
 download it manually from the following links and put it in '~/.EasyOCR/model' folder
@@ -73,8 +73,14 @@ download it manually from the following links and put it in '~/.EasyOCR/model' f
 Output will be in list format, each item represents bounding box, text and confident level, respectively.
 
 ``` bash
-[([[1344, 439], [2168, 439], [2168, 580], [1344, 580]], 'ใจเด็ด', 0.4542357623577118),
- ([[1333, 562], [2169, 562], [2169, 709], [1333, 709]], 'Project', 0.9557611346244812)]
+[([[189, 75], [469, 75], [469, 165], [189, 165]], '愚園路', 0.97784423828125),
+ ([[86, 80], [134, 80], [134, 128], [86, 128]], '西', 0.9951117038726807),
+ ([[517, 81], [565, 81], [565, 123], [517, 123]], '東', 0.9558971524238586),
+ ([[78, 126], [136, 126], [136, 156], [78, 156]], '315', 0.6018078923225403),
+ ([[514, 126], [574, 126], [574, 156], [514, 156]], '309', 0.8362029194831848),
+ ([[226, 170], [414, 170], [414, 220], [226, 220]], 'Yuyuan Rd.', 0.8912249207496643),
+ ([[79, 173], [125, 173], [125, 213], [79, 213]], 'W', 0.9854364395141602),
+ ([[529, 173], [569, 173], [569, 213], [529, 213]], 'E', 0.5593774318695068)]
 ```
 
 In case you do not have GPU or your GPU has low memory, you can run it in CPU mode by adding gpu = False
@@ -83,7 +89,7 @@ In case you do not have GPU or your GPU has low memory, you can run it in CPU mo
 reader = easyocr.Reader(['th','en'], gpu = False)
 ```
 
-There are optional arguments for readtext function, `decoder` can be 'greedy'(default), 'beamsearch', or 'wordbeamsearch'. For 'beamsearch' and 'wordbeamsearch', you can also set `beamWidth` (default=5). Bigger number will be slower but can be more accurate. For multiprocessing, you can set `workers` and `batch_size`. Current version converts image into grey scale for recognition model, so contrast can be an issue. You can try playing with `contrast_ths`, `adjust_contrast` and `filter_ths`.
+There are optional arguments for readtext function, `decoder` can be 'greedy'(default), 'beamsearch', or 'wordbeamsearch'. For 'beamsearch' and 'wordbeamsearch', you can also set `beamWidth` (default=5). Bigger number will be slower but can be more accurate. For multiprocessing, you can set `workers` and `batch_size`. Current version converts image into grey scale for recognition model, so contrast can be an issue. You can try playing with `contrast_ths`, `adjust_contrast` and `filter_ths`. `whitelist` and `blacklist` accept input in string (like this blacklist = '!&$%').
 
 ## To be implemented
 
@@ -119,4 +125,3 @@ To request a new language support, I need you to send a PR with 2 following file
 If your language has unique elements (such as 1. Arabic: characters change form when attach to each other + write from right to left 2. Thai: Some characters need to be above the line and some below), please educate me with your best ability and/or give useful links. It is important to take care of the detail to achieve a system that really works.
 
 Lastly, please understand that my priority will have to go to popular language or set of languages that share most of characters together (also tell me if your language share a lot of characters with other). It takes me at least a week to work for new model. You may have to wait a while for new model to be released.
-