dannguyen · July 29, 2025 14:26 · Mar 26, 2016 · Mar 26, 2016 · Mar 24, 2016 · Mar 24, 2016
diff --git a/README.md b/README.md
@@ -1,4 +1,4 @@
-# Using Google Cloud Vision API's OCR to extract text from photos and scanned documents
+# Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents
 
 Just a quickie test in Python 3 (using Requests) to see if [Google Cloud Vision](https://cloud.google.com/vision) can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as [ABBYY FineReader can OCR an image and provide Excel-ready output](https://github.com/dannguyen/abbyy-finereader-ocr-senate).
 

diff --git a/README.md b/README.md
@@ -177,6 +177,8 @@ BAKER, BENITA
 
 __Tesseract (version 3.04 as of Feb. 2016)__, [the open-source OCR tool currently maintained by Google](https://github.com/tesseract-ocr/tesseract), doesn't perform meaningfully in the photo of the road signs, but it is generally pretty strong in the case of screenshotted text. For comparison's sake, here is its output of the tabular data example -- unlike the Cloud Vision API, it *does* attempt to preserve some of the line layout -- and it has the option of [providing HOCR output which can then be used to further define spatial layout](https://github.com/jsfenfen/whatwordwhere).
 
+(note: I've preserved the whitespace in the output, so there are a few dozen blank lines at the bottom...keep scrolling to get to the next section...)
+
 ~~~
  
 

diff --git a/README.md b/README.md
@@ -12,17 +12,20 @@ On the other hand, the OCR quality is pretty good, if you just need to identify
 
 ![Jughandle_signage](https://upload.wikimedia.org/wikipedia/commons/e/e4/Jughandle_signage.jpg)
 
+
+###### 1a. Google GMail CAPTCHA (circa 2009)
+
+Added this out of curiousity: a sample taken from Google's 2009 research paper, [What’s Up CAPTCHA?; A CAPTCHA Based On Image Orientation](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35157.pdf)
+
+![captcha](http://i.imgur.com/3lu00lM.jpg)
+
+
 ####### 2. An image (PDF to PNG) of a spreadsheet
 
 [Courtesy of Eli Lilly](http://www.lillyphysicianpaymentregistry.com/Payments-to-Physicians):
 
 ![image](https://cloud.githubusercontent.com/assets/121520/14005729/4bf2648e-f123-11e5-84d6-be1c9d84cdcd.png)
 
-###### 3. Google GMail CAPTCHA (circa 2009)
-
-Added this out of curiousity: a sample taken from Google's 2009 research paper, [What’s Up CAPTCHA?; A CAPTCHA Based On Image Orientation](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35157.pdf)
-
-![captcha](http://i.imgur.com/3lu00lM.jpg)
 
 
 

diff --git a/README.md b/README.md
@@ -18,6 +18,13 @@ On the other hand, the OCR quality is pretty good, if you just need to identify
 
 ![image](https://cloud.githubusercontent.com/assets/121520/14005729/4bf2648e-f123-11e5-84d6-be1c9d84cdcd.png)
 
+###### 3. Google GMail CAPTCHA (circa 2009)
+
+Added this out of curiousity: a sample taken from Google's 2009 research paper, [What’s Up CAPTCHA?; A CAPTCHA Based On Image Orientation](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35157.pdf)
+
+![captcha](http://i.imgur.com/3lu00lM.jpg)
+
+
 
 You can [read more about getting started with the Google Cloud Vision API in its official docs](https://cloud.google.com/vision/docs/getting-started). My Python script is a somewhat simplified version of the official instructions here:
 
@@ -64,6 +71,17 @@ A
 
 ~~~
 
+### GMail Captcha
+
+Sorry spammers, you probably won't get far using Google's API against its old CAPTCHA system (nevermind its [current one](https://www.google.com/recaptcha/intro/index.html)):
+
+~~~py
+    Bounding Polygon:
+{'vertices': [{'y': 73, 'x': 142}, {'y': 73, 'x': 339}, {'y': 173, 'x': 339}, {'y': 173, 'x': 142}]}
+    Text:
+ngly:h
+~~~
+
 
 ### Spreadsheet
 

diff --git a/README.md b/README.md
@@ -323,4 +323,26 @@ https://github.com/GoogleCloudPlatform/cloud-vision/tree/master/python/text
 
 It includes using the __googleapiclient__ library, which has various conveniences including a `num_retries` argument.
 
-
+#### Authenticating via oauth2 JSON credentials
+
+If you want to use the __googleapiclient__, which includes authenticating via oauth2 credentials, here's a variation of how to authenticate a service request as shown in the official docs, except from a given filename (i.e. as opposed to setting an environment variable and calling `GoogleCredentials.get_application_default()`):
+
+~~~py
+from googleapiclient import discovery
+from oauth2client.client import GoogleCredentials
+DISCOVERY_URL = 'https://{api}.googleapis.com/$discovery/rest?version={apiVersion}'
+def get_vision(oauth2_creds_filename, service_url=DISCOVERY_URL):
+    """
+    Read oauth2 credentials and return a Google service object,
+      which you can then invoke like this:
+
+    ("vision" is the service object)
+    request = vision.images().annotate(body={'requests': img_requests_data})
+    vision_response_dict = request.execute(num_retries=5)
+
+    """
+    creds = GoogleCredentials.from_stream(oauth2_creds_filename)
+    service = discovery.build('vision', 'v1', credentials=creds,
+                              discoveryServiceUrl=DISCOVERY_URL)
+    return service
+~~~
diff --git a/README.md b/README.md
@@ -317,4 +317,10 @@ Cloud Vision probably isn't intended for picking apart text documents. Occasiona
 ~~~
 
 
+For a more robust experience, you probably want to follow the example linked from the API's official docs:
+
+https://github.com/GoogleCloudPlatform/cloud-vision/tree/master/python/text
+
+It includes using the __googleapiclient__ library, which has various conveniences including a `num_retries` argument.
+
 
diff --git a/README.md b/README.md
@@ -299,11 +299,11 @@ BAKER, BENITA DECATUR IL BAKER, BENITA $17,100 $11,738 $6,884 $35,722
 
 # Performance and latency
 
-I didn't rigorously test this so these are just rough averages/medians:
+I didn't rigorously test this so these are just rough averages/medians of how long it took for the entire script (including any network latency) to complete:
 
 * __Road signs:__ 2.1 seconds
 * __Spreadsheet:__ 6.8 seconds
-* __Spreadsheet (with Tesseract)__: 4.1 seconds
+* __Spreadsheet (using Tesseract)__: 4.1 seconds
 
 Cloud Vision probably isn't intended for picking apart text documents. Occasionally, the API would fail on the spreadsheet image with this result:
 

diff --git a/README.md b/README.md
@@ -297,3 +297,24 @@ BAKER, BENITA DECATUR IL BAKER, BENITA $17,100 $11,738 $6,884 $35,722
 ~~~
 
 
+# Performance and latency
+
+I didn't rigorously test this so these are just rough averages/medians:
+
+* __Road signs:__ 2.1 seconds
+* __Spreadsheet:__ 6.8 seconds
+* __Spreadsheet (with Tesseract)__: 4.1 seconds
+
+Cloud Vision probably isn't intended for picking apart text documents. Occasionally, the API would fail on the spreadsheet image with this result:
+
+~~~json
+{
+  "error": {
+    "code": 4,
+    "message": "image-annotator::RPC deadline exceeded.: Backend timeout!"
+  }
+}
+~~~
+
+
+
diff --git a/cloudvisreq.py b/cloudvisreq.py
@@ -51,9 +51,8 @@ def request_ocr(api_key, image_filenames):
             $ python cloudvisreq.py api_key image1.jpg image2.png""")
     else:
         response = request_ocr(api_key, image_filenames)
-        if response.status_code != 200:
+        if response.status_code != 200 or response.json().get('error'):
             print(response.text)
-
         else:
             for idx, resp in enumerate(response.json()['responses']):
                 # save to JSON file

diff --git a/README.md b/README.md
@@ -154,7 +154,7 @@ BAKER, BENITA
 
 ### Tesseract comparison 
 
-__Tesseract (version 3.04 as of Feb. 2016)__, [the open-source OCR tool currently maintained by Google](https://github.com/tesseract-ocr/tesseract), doesn't perform meaningfully in the photo of the road signs, but it is generally pretty strong in the case of screenshotted text. For comparison's sake, here is its output of the tabular data example:
+__Tesseract (version 3.04 as of Feb. 2016)__, [the open-source OCR tool currently maintained by Google](https://github.com/tesseract-ocr/tesseract), doesn't perform meaningfully in the photo of the road signs, but it is generally pretty strong in the case of screenshotted text. For comparison's sake, here is its output of the tabular data example -- unlike the Cloud Vision API, it *does* attempt to preserve some of the line layout -- and it has the option of [providing HOCR output which can then be used to further define spatial layout](https://github.com/jsfenfen/whatwordwhere).
 
 ~~~
  

diff --git a/README.md b/README.md
@@ -150,4 +150,150 @@ DALLAS BAILEY-GRAY, PATRICK 226 $226
 BAILEY-GRAY, PATRICK
 DECATUR IL BAKER, BENITA $17.100 S11738 $6,884 $35,722
 BAKER, BENITA
-~~~
+~~~
+
+### Tesseract comparison 
+
+__Tesseract (version 3.04 as of Feb. 2016)__, [the open-source OCR tool currently maintained by Google](https://github.com/tesseract-ocr/tesseract), doesn't perform meaningfully in the photo of the road signs, but it is generally pretty strong in the case of screenshotted text. For comparison's sake, here is its output of the tabular data example:
+
+~~~
+ 
+
+o Lilly Other Health Care Professional Registry
+Payments Made: Q1-Q4 2013 Data updated on Monday, March 3, 2014
+
+ 
+
+The Other Health Care Professional Registry reports direct and indirect payments by Lilly, as well as Lilly's portion of alliance partnership payments to health care professionals other than physicians serving as faculty
+members. When the "Entity Paid" is a company, hospital, or university, and there is a different name on the provider of service, it reﬂects an indirect payment which may or may not actually have been received in
+whole or in part by the listed service provider. Copyright © 2014 Eli Lilly and Company. All rights reserved.
+
+Note: Due to differences in scope and deﬁnitions, the data reported in this report may differ from data included in reports submitted by Lilly for compliance with state payment reporting laws.
+
+ 
+
+ 
+
+ 
+
+ 
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+ 
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+ 
+
+ 
+
+ 
+
+Payments*
+(All Amounts in US Dollars)
+Provider of Service *Payments for re u bursed expenses are not compensatio
+Advising]
+H
+Patient Education “2:13:33 consulting & Certain
+Name Location State Name . International Travel-Related 2013 Total
+Programs Education .
+Programs Education Expenses
+Programs
+A1 CERTIFIED DIABETES EDUCATORS,
+LLC GILBERT AZ WARD, JAN ET $300 $1,631 $129 $2,060
+ABDALLAH, RITA FAIRVIEW PARK OH ABDALLAH, RITA $14,025 $500 $2,823 $17,348
+ADAMS, MARY ELLEN CINCINNATI OH ADAMS, MARY ELLEN $3,450 $1,594 $5,044
+AGOSTI, CAROL TOLEDO OH AGOSTI, CAROL $1,050 $1,631 $355 $3,036
+AINSWORTH, ABBY CORPUS CHRISTI TX AINSWORTH, ABBY $113 $113
+AKERS, REBECCA VINCENNES IN AKERS, REBECCA $2,400 $2,400
+ALCHEMIPHARMA LLC, WAYNE PA BELAZI, DEA $11,889 $199 $12,088
+ALEMAN, MARY MEADOW VISTA CA ALEMAN, MARY $3,300 $1,294 $346 $4,940
+ALEXANDER, LISA EVANSVILLE IN ALEXANDER, LISA $3,000 $1,988 $1,077 $6,065
+ALLEN, CONNETI'E COTTAGE GROVE MN ALLEN, CONNETI'E $6,113 $875 $2,347 $9,335
+ALLISON HEINRICH, L.L. LUBBOCK TX HEINRICH, ALLISON $4,200 $1,238 $5,438
+ALLISON, CRYSTAL GOODYEAR AZ ALLISON, CRYSTAL $250 $250
+ALLISON, SUSAN EASTON PA ALLISON, SUSAN $7,950 $2,138 $2,252 $12,340
+ALVAREZ, MICHELLE MCALLEN TX ALVAREZ, MICHELLE $4,650 $750 $244 $5,644
+ANDARIESE, JUDITH BROOKLYN NY AN DARIESE, JUDITH $12,450 $338 $1,029 $13,817
+ANGELES WORLD, INC., GRANADA HILLS CA ANGELES, ADA $39,150 $11,456 $938 $9,181 $60,725
+ARBOLEDA, JANE MIAMI FL ARBOLEDA, JANE $375 $4,875 $549 $5,799
+ARMSTRONG, JULIE WACO TX ARMSTRONG, JULIE $2,400 $750 $3,150
+ARNOLD, MARY BETH AUGUSTA GA ARNOLD, MARY BETH $900 $2,006 $657 $3,563
+ARRAMBIDE, ROBIN KATY TX ARRAMBIDE, ROBIN $1,719 $1,719
+ARRINGTON, WANDA CHARLOTTE NC ARRINGTON, WANDA $2,531 $258 $2,789
+ASHFORD, RICHARD WILKES BARRE PA ASHFORD, RICHARD $3,525 $339 $3,864
+ATTANASIO, MICHAEL SEWELL NJ AlTANASIO, MICHAEL $450 $450
+AVOLIO, JOHN INDIANA PA AVOLIO, JOHN $225 $225
+BABEY, CHRISTINE SCOTTSDALE AZ BABEY, CHRISTINE $5,100 $13,294 $2,135 $20,529
+BAILEY-G RAY, PATRICK DALLAS TX BAILEY-G RAY, PATRICK $226 $226
+BAIRD, DENISE LANCASTER PA BAIRD, DENISE $3,000 $638 $176 $3,814
+BAKER, BENITA DECATUR IL BAKER, BENITA $17,100 $11,738 $6,884 $35,722
+
+ 
+
+ 
+
+~~~
+
+
diff --git a/README.md b/README.md
@@ -1,18 +1,18 @@
-# Using Google Cloud Vision API to OCR scanned documents to extract structured data
+# Using Google Cloud Vision API's OCR to extract text from photos and scanned documents
 
-Just a quickie test in Python 3 (using Requests) to see if [Google Cloud Vision](https://cloud.google.com/vision) can be used to effectively OCR a scanned data table, in the way that products like [ABBYY FineReader provide image-ocr-to-Excel](https://github.com/dannguyen/abbyy-finereader-ocr-senate).
+Just a quickie test in Python 3 (using Requests) to see if [Google Cloud Vision](https://cloud.google.com/vision) can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as [ABBYY FineReader can OCR an image and provide Excel-ready output](https://github.com/dannguyen/abbyy-finereader-ocr-senate).
 
 __The short answer:__ No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
 
-On the other hand, the OCR quality is pretty good. I've included two examples: 
+On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples: 
 
-####### 1. An image of road signs
+####### 1. A low-resolution photo of road signs
 
 [Courtesy of SPUI on Wikicommons](https://commons.wikimedia.org/wiki/File:Jughandle_signage.jpg):
 
 ![Jughandle_signage](https://upload.wikimedia.org/wikipedia/commons/e/e4/Jughandle_signage.jpg)
 
-####### 2. An image of a spreadsheet
+####### 2. An image (PDF to PNG) of a spreadsheet
 
 [Courtesy of Eli Lilly](http://www.lillyphysicianpaymentregistry.com/Payments-to-Physicians):
 

diff --git a/README.md b/README.md
@@ -1,9 +1,24 @@
 # Using Google Cloud Vision API to OCR scanned documents to extract structured data
 
-Just a quickie test to see if [Google Cloud Vision](https://cloud.google.com/vision) can be used to effectively OCR a scanned data table, in the way that products like [ABBYY FineReader provide image-ocr-to-Excel](https://github.com/dannguyen/abbyy-finereader-ocr-senate).
+Just a quickie test in Python 3 (using Requests) to see if [Google Cloud Vision](https://cloud.google.com/vision) can be used to effectively OCR a scanned data table, in the way that products like [ABBYY FineReader provide image-ocr-to-Excel](https://github.com/dannguyen/abbyy-finereader-ocr-senate).
 
 __The short answer:__ No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
 
+On the other hand, the OCR quality is pretty good. I've included two examples: 
+
+####### 1. An image of road signs
+
+[Courtesy of SPUI on Wikicommons](https://commons.wikimedia.org/wiki/File:Jughandle_signage.jpg):
+
+![Jughandle_signage](https://upload.wikimedia.org/wikipedia/commons/e/e4/Jughandle_signage.jpg)
+
+####### 2. An image of a spreadsheet
+
+[Courtesy of Eli Lilly](http://www.lillyphysicianpaymentregistry.com/Payments-to-Physicians):
+
+![image](https://cloud.githubusercontent.com/assets/121520/14005729/4bf2648e-f123-11e5-84d6-be1c9d84cdcd.png)
+
+
 You can [read more about getting started with the Google Cloud Vision API in its official docs](https://cloud.google.com/vision/docs/getting-started). My Python script is a somewhat simplified version of the official instructions here:
 
 https://github.com/GoogleCloudPlatform/cloud-vision/tree/master/python/text
@@ -22,19 +37,40 @@ The __cloudvisreq.py__ script is included at the bottom of this gist.
 $  python cloudvisreq.py API_KEY image1.jpg image2.png
 ~~~
 
-### Sample image:
 
-[Courtesy of Eli Lilly](http://www.lillyphysicianpaymentregistry.com/Payments-to-Physicians):
 
-![image](https://cloud.githubusercontent.com/assets/121520/14005729/4bf2648e-f123-11e5-84d6-be1c9d84cdcd.png)
+
+## Results
+
+### Road signs
+
+~~~
+    Bounding Polygon:
+{'vertices': [{'x': 16, 'y': 21}, {'x': 772, 'y': 21}, {'x': 772, 'y': 322}, {'x': 16, 'y': 322}]}
+    Text:
+WARRENVILLE RD
+NORTH
+SOUTH
+ALL TURNS
+FROM
+WASHINGTON AVE
+RIGHT LANE
+GREEN BROOK
+U AND LEFT
+DUNELLEN
+TURNS
+ALL TURNS f
+A
+
+~~~
+
+
+### Spreadsheet
 
 
 
-### Result:
 
 ~~~
-Wrote 3021 bytes to jsons/pdftable.png.json
----------------------------------------------
     Bounding Polygon:
 {'vertices': [{'y': 272, 'x': 212}, {'y': 272, 'x': 3066}, {'y': 2295, 'x': 3066}, {'y': 2295, 'x': 212}]}
     Text:

diff --git a/cloudvisreq.py b/cloudvisreq.py
@@ -1,14 +1,20 @@
 from base64 import b64encode
-from sys import argv
 from os import makedirs
 from os.path import join, basename
+from sys import argv
 import json
 import requests
 
 ENDPOINT_URL = 'https://vision.googleapis.com/v1/images:annotate'
 RESULTS_DIR = 'jsons'
 makedirs(RESULTS_DIR, exist_ok=True)
-def make_image_data_dict(image_filenames):
+
+def make_image_data_list(image_filenames):
+    """
+    image_filenames is a list of filename strings
+    Returns a list of dicts formatted as the Vision API
+        needs them to be
+    """
     img_requests = []
     for imgname in image_filenames:
         with open(imgname, 'rb') as f:
@@ -19,21 +25,20 @@ def make_image_data_dict(image_filenames):
                         'type': 'TEXT_DETECTION',
                         'maxResults': 1
                     }]
-                }
-            )
+            })
     return img_requests
 
 def make_image_data(image_filenames):
-    imgdict = make_image_data_dict(image_filenames)
+    """Returns the image data lists as bytes"""
+    imgdict = make_image_data_list(image_filenames)
     return json.dumps({"requests": imgdict }).encode()
 
 
 def request_ocr(api_key, image_filenames):
-    imgdata = make_image_data(image_filenames)
     response = requests.post(ENDPOINT_URL,
-                        data=imgdata,
-                        params={'key': api_key},
-                        headers={'Content-Type': 'application/json'})
+                             data=make_image_data(image_filenames),
+                             params={'key': api_key},
+                             headers={'Content-Type': 'application/json'})
     return response
 
 
@@ -51,16 +56,18 @@ def request_ocr(api_key, image_filenames):
 
         else:
             for idx, resp in enumerate(response.json()['responses']):
+                # save to JSON file
                 imgname = image_filenames[idx]
-                datatxt = json.dumps(resp, indent=2)
                 jpath = join(RESULTS_DIR, basename(imgname) + '.json')
                 with open(jpath, 'w') as f:
+                    datatxt = json.dumps(resp, indent=2)
+                    print("Wrote", len(datatxt), "bytes to", jpath)
                     f.write(datatxt)
 
-                print("Wrote", len(datatxt), "bytes to", jpath)
+                # print the plaintext to screen for convenience
                 print("---------------------------------------------")
-                txtan = resp['textAnnotations'][0]
+                t = resp['textAnnotations'][0]
                 print("    Bounding Polygon:")
-                print(txtan['boundingPoly'])
+                print(t['boundingPoly'])
                 print("    Text:")
-                print(txtan['description'])
+                print(t['description'])
diff --git a/README.md b/README.md
@@ -4,11 +4,11 @@ Just a quickie test to see if [Google Cloud Vision](https://cloud.google.com/vis
 
 __The short answer:__ No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
 
-You can [read more about getting started with the Google Cloud Vision API in its official docs](https://cloud.google.com/vision/docs/getting-started). The instructions here are a somewhat simplified version of the official instructions here:
+You can [read more about getting started with the Google Cloud Vision API in its official docs](https://cloud.google.com/vision/docs/getting-started). My Python script is a somewhat simplified version of the official instructions here:
 
 https://github.com/GoogleCloudPlatform/cloud-vision/tree/master/python/text
 
-Set up a Google developer account and get an API key:
+You first have to set up a Google developer account and get an API key (the API allows 1000 free requests a month):
 
 https://cloud.google.com/vision/docs/auth-template/cloud-api-auth#set_up_an_api_key
 

diff --git a/README.md b/README.md
@@ -1,12 +1,36 @@
+# Using Google Cloud Vision API to OCR scanned documents to extract structured data
+
+Just a quickie test to see if [Google Cloud Vision](https://cloud.google.com/vision) can be used to effectively OCR a scanned data table, in the way that products like [ABBYY FineReader provide image-ocr-to-Excel](https://github.com/dannguyen/abbyy-finereader-ocr-senate).
+
+__The short answer:__ No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
+
+You can [read more about getting started with the Google Cloud Vision API in its official docs](https://cloud.google.com/vision/docs/getting-started). The instructions here are a somewhat simplified version of the official instructions here:
+
+https://github.com/GoogleCloudPlatform/cloud-vision/tree/master/python/text
+
+Set up a Google developer account and get an API key:
+
+https://cloud.google.com/vision/docs/auth-template/cloud-api-auth#set_up_an_api_key
+
 
 
 ## How to run
 
+The __cloudvisreq.py__ script is included at the bottom of this gist.
+
 ~~~sh
 $  python cloudvisreq.py API_KEY image1.jpg image2.png
 ~~~
 
-Result:
+### Sample image:
+
+[Courtesy of Eli Lilly](http://www.lillyphysicianpaymentregistry.com/Payments-to-Physicians):
+
+![image](https://cloud.githubusercontent.com/assets/121520/14005729/4bf2648e-f123-11e5-84d6-be1c9d84cdcd.png)
+
+
+
+### Result:
 
 ~~~
 Wrote 3021 bytes to jsons/pdftable.png.json

diff --git a/README.md b/README.md
@@ -1 +1,93 @@
-test
+
+
+## How to run
+
+~~~sh
+$  python cloudvisreq.py API_KEY image1.jpg image2.png
+~~~
+
+Result:
+
+~~~
+Wrote 3021 bytes to jsons/pdftable.png.json
+---------------------------------------------
+    Bounding Polygon:
+{'vertices': [{'y': 272, 'x': 212}, {'y': 272, 'x': 3066}, {'y': 2295, 'x': 3066}, {'y': 2295, 'x': 212}]}
+    Text:
+Lilly other Health Care Professional Registry
+Data updated on Monday, March 3, 2014
+Payments Made: Q1-Q4 2013
+The Other Health Care Professional Registry reports direct and indirect payments by Lilly, as well as Lilly's portion of alliance partnership paymen
+o health care professionals other than physicians serving as faculty
+members. When the "Entity Paid
+a company, hospital, or university, and there
+a different name on the provider of service
+reflects an indirect payment which may or may not actually have been received in
+whole or in part by the
+ted service provider. Copyright 2014 Eli Lilly and Company. All rights reserved
+Note: Due to differences in scope and definitions, the data reported in this report may differ from data included in reports submitted by Lilly for compliance with state payment reporting laws
+Payments
+All Amounts in US Dollars
+Provider of Service
+*Payments for reimbursed expenses are not compensation
+Entity Paid
+Patient Education Professional
+Education
+Education
+WARD, JANET
+OH ABDALLAH, RITA $14,025 $500 $2,823 S17348
+ABDALLAH, RITA
+FAIRVIEW PARK
+OH AGOSTI, CAROL $1,050 1,631 $355 $3036
+AGOSTI, CAROL
+TOLEDO
+AINSWORTH, ABBY CORPUS CHRISTI AINSWORTH, ABBY $113 $113
+AKERS, REBECCA VINCENNES IN AKERS, REBECCA
+$2,400 $2,400
+IALCHEMIPHARMA LLC, WAYNE PA BELAZI, DEA $11,889 $1.99 $12,088
+MEADOW VISTA
+ALEMAN, MARY
+ALEMAN, MARY
+ALEXANDER, LISA
+ALEXANDER, LISA
+FALLEN, CONNETTE COTTAGE GROVE
+MN ALLEN, CONNETTE $6,113 $875 $2,347 $9,335
+MALLISON HEINRICH, LL. LUBBOCK HEINRICH, ALLISON $4,200 $1.238 $5.438
+ALLISON, CRYSTAL GOODYEAR
+AZ ALLISON, CRYSTAL s250 $250
+MALLISON, SUSAN
+EASTON
+PA ALLISON, SUSAN $7950 $2.138 $2.252 $12.340
+ALVAREZ, MICHELLE MCALLEN TX ALVAREZ, MICHELLE
+S4,650 $750 $244 $5,644
+GRANADA HILLS CA ANGELES, ADA
+$39,150 $11,456 $938 $9,181 $60,725
+ANGELES WORLD, INC.,
+ARBOLEDA, JANE
+MIAMI FL ARBOLEDA, JANE
+S375 $4,875 $549 $5,799
+WACO ARMSTRONG, JULIE
+$2,400 $750 $3,150
+ARMSTRONG, JULIE
+S900 $2,006 $657 $3,563
+ARNOLD, MARY BETH
+AUGUSTA
+GA ARNOLD, MARY BETH
+ARRAMBIDE, ROBIN KATY TX ARRAMBIDE, ROBIN
+$11719 $11719
+ARRINGTON, WANDA CHARLOTTE NC ARRINGTON, WANDA
+S2,531 $258 $2789
+PA ASH FORD, RICHARD
+$3,525 S339 $3,864
+WILKES BARRE
+ASHFORD, RICHARD
+ATTANASIO, MICHAEL SEWELL NJ ATTANASIO, MICHAEL
+$450 $450
+INDIANA PA AvOLIO JOHN $225 $225
+AVOLIO JOHN
+BABEY, CHRISTINE SCOTTSDALE AZ BABEY, CHRISTINE $5.100 $13,294 $2.135 $20,529
+DALLAS BAILEY-GRAY, PATRICK 226 $226
+BAILEY-GRAY, PATRICK
+DECATUR IL BAKER, BENITA $17.100 S11738 $6,884 $35,722
+BAKER, BENITA
+~~~
diff --git a/cloudvisreq.py b/cloudvisreq.py
@@ -0,0 +1,66 @@
+from base64 import b64encode
+from sys import argv
+from os import makedirs
+from os.path import join, basename
+import json
+import requests
+
+ENDPOINT_URL = 'https://vision.googleapis.com/v1/images:annotate'
+RESULTS_DIR = 'jsons'
+makedirs(RESULTS_DIR, exist_ok=True)
+def make_image_data_dict(image_filenames):
+    img_requests = []
+    for imgname in image_filenames:
+        with open(imgname, 'rb') as f:
+            ctxt = b64encode(f.read()).decode()
+            img_requests.append({
+                    'image': {'content': ctxt},
+                    'features': [{
+                        'type': 'TEXT_DETECTION',
+                        'maxResults': 1
+                    }]
+                }
+            )
+    return img_requests
+
+def make_image_data(image_filenames):
+    imgdict = make_image_data_dict(image_filenames)
+    return json.dumps({"requests": imgdict }).encode()
+
+
+def request_ocr(api_key, image_filenames):
+    imgdata = make_image_data(image_filenames)
+    response = requests.post(ENDPOINT_URL,
+                        data=imgdata,
+                        params={'key': api_key},
+                        headers={'Content-Type': 'application/json'})
+    return response
+
+
+if __name__ == '__main__':
+    api_key, *image_filenames = argv[1:]
+    if not api_key or not image_filenames:
+        print("""
+            Please supply an api key, then one or more image filenames
+
+            $ python cloudvisreq.py api_key image1.jpg image2.png""")
+    else:
+        response = request_ocr(api_key, image_filenames)
+        if response.status_code != 200:
+            print(response.text)
+
+        else:
+            for idx, resp in enumerate(response.json()['responses']):
+                imgname = image_filenames[idx]
+                datatxt = json.dumps(resp, indent=2)
+                jpath = join(RESULTS_DIR, basename(imgname) + '.json')
+                with open(jpath, 'w') as f:
+                    f.write(datatxt)
+
+                print("Wrote", len(datatxt), "bytes to", jpath)
+                print("---------------------------------------------")
+                txtan = resp['textAnnotations'][0]
+                print("    Bounding Polygon:")
+                print(txtan['boundingPoly'])
+                print("    Text:")
+                print(txtan['description'])
diff --git a/readme.md b/readme.md
@@ -1 +0,0 @@
-xxx

diff --git a/README.md b/README.md
@@ -0,0 +1 @@
+test
diff --git a/readme.md b/readme.md
@@ -0,0 +1 @@
+xxx