Xiaolu Xiong beardeer

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 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.

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, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

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start new:

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start new with session name:

tmux new -s myname

	import numpy as np

	from caffe2.python import (
	brew,
	model_helper,
	optimizer,
	workspace,
	utils,
	)
	from caffe2.proto import caffe2_pb2

	#! /usr/bin/env python
	# -- coding: utf-8 --
	"""This module's docstring summary line.

	This is a multi-line docstring. Paragraphs are separated with blank lines.
	Lines conform to 79-column limit.

	Module and packages names should be short, lower_case_with_underscores.
	Notice that this in not PEP8-cheatsheet.py

	import pandas as pd
	from random import random

	flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000
	pdata = pd.DataFrame({"a":flow, "b":flow})
	pdata.b = pdata.b.shift(9)
	data = pdata.iloc[10:] * random() # some noise

	import numpy as np

	import pandas as pd
	from random import random

	flow = (list(range(1,10,1)) + list(range(10,1,-1)))*100
	pdata = pd.DataFrame({"a":flow, "b":flow})
	pdata.b = pdata.b.shift(9)
	data = pdata.iloc[10:] * random() # some noise

	import numpy as np

	import theano
	import theano.tensor as T
	import numpy as np
	import cPickle
	import random
	import matplotlib.pyplot as plt

	class RNN(object):

	def __init__(self, nin, n_hidden, nout):

	{
	"metadata": {
	"name": "",
	"signature": "sha256:a04c38d9604adb7eb9ca89860dfa1ef72db66037cc2c07c391ef8e67a31f9254"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{

	.
	├── deploy.py
	├── project
	│ ├── application.py
	│ ├── apps
	│ │ ├── articles
	│ │ │ ├── forms.py
	│ │ │ ├── __init__.py
	│ │ │ ├── models.py
	│ │ │ └── views.py

	#!/usr/bin/env python

	from numpy import asmatrix, asarray, ones, zeros, mean, sum, arange, prod, dot, loadtxt
	from numpy.random import random, randint
	import pickle

	MISSING_VALUE = -1 # a constant I will use to denote missing integer values

	def impute_hidden_node(E, I, theta, sample_hidden):