Commit bb8bd034 authored by francois's avatar francois

erp5_receipt_recognition: Execute OCR on receipt image to find total value

This commit contain the business template that take a receipt image as a source,
binarize then segmentize it, and apply OCR on it. It then extract the meaning
with regular expressions. The image should already be loaded inside the
image module before it can be read.

The business template contain:
	* The receipt recognition module
	* An extension containing the code that binarize, crop and
	  segmentize the image then analize it.
	* A new type "Receipt" that contain a source image and the
	  field that contain the "total" value
	* A portal skin folder containing the extension externalMethods
	  aswell as the conversion script that call the recognition and
	  update the Receipt "total" field

Improvements (not limited to this list):
	- Easier loading of picture: directly from the receipt page.
	- Easier loading of picture 2: from phone with OfficeJS
	  (or any renderJS) application?
	- Detect when images are sideway and rotate them straight
	- Better "boxing" and segmentation: some lines are deleted from
	  the original image during the segmentation when they are too
	  close from other
	- Modify the neural network (lstm) to increase weight of signs
	  like $, euro, / and numbers
	- Use of a faster/smaller neural network: Most of the time is
	  lost with the loading of the neural network
	- Caching the neural network: See previous statement.
	- Extract currency, date and receipt emettor.
	- Use a neural network for the meaning extraction?
parent ec73ce52
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="ActionInformation" module="Products.CMFCore.ActionInformation"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>action</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAI=</string> </persistent>
</value>
</item>
<item>
<key> <string>categories</string> </key>
<value>
<tuple>
<string>action_type/object_list</string>
</tuple>
</value>
</item>
<item>
<key> <string>category</string> </key>
<value> <string>object_list</string> </value>
</item>
<item>
<key> <string>condition</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>description</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>icon</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>view</string> </value>
</item>
<item>
<key> <string>permissions</string> </key>
<value>
<tuple>
<string>View</string>
</tuple>
</value>
</item>
<item>
<key> <string>portal_type</string> </key>
<value> <string>Action Information</string> </value>
</item>
<item>
<key> <string>priority</string> </key>
<value> <float>1.0</float> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string>View</string> </value>
</item>
<item>
<key> <string>visible</string> </key>
<value> <int>1</int> </value>
</item>
</dictionary>
</pickle>
</record>
<record id="2" aka="AAAAAAAAAAI=">
<pickle>
<global name="Expression" module="Products.CMFCore.Expression"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>text</string> </key>
<value> <string>string:${object_url}/ReceiptRecognitionModule_viewReceiptRecognitionList</string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="ActionInformation" module="Products.CMFCore.ActionInformation"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>action</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAI=</string> </persistent>
</value>
</item>
<item>
<key> <string>categories</string> </key>
<value>
<tuple>
<string>action_type/object_action</string>
</tuple>
</value>
</item>
<item>
<key> <string>category</string> </key>
<value> <string>object_action</string> </value>
</item>
<item>
<key> <string>condition</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>description</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>icon</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>receipt_convert</string> </value>
</item>
<item>
<key> <string>permissions</string> </key>
<value>
<tuple>
<string>Modify portal content</string>
</tuple>
</value>
</item>
<item>
<key> <string>portal_type</string> </key>
<value> <string>Action Information</string> </value>
</item>
<item>
<key> <string>priority</string> </key>
<value> <float>1.0</float> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string>Convert Receipt</string> </value>
</item>
<item>
<key> <string>visible</string> </key>
<value> <int>1</int> </value>
</item>
</dictionary>
</pickle>
</record>
<record id="2" aka="AAAAAAAAAAI=">
<pickle>
<global name="Expression" module="Products.CMFCore.Expression"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>text</string> </key>
<value> <string>string:${object_url}/ReceiptConversion_convertImage</string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="ActionInformation" module="Products.CMFCore.ActionInformation"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>action</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAI=</string> </persistent>
</value>
</item>
<item>
<key> <string>categories</string> </key>
<value>
<tuple>
<string>action_type/object_view</string>
</tuple>
</value>
</item>
<item>
<key> <string>category</string> </key>
<value> <string>object_view</string> </value>
</item>
<item>
<key> <string>condition</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>description</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>icon</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>view</string> </value>
</item>
<item>
<key> <string>permissions</string> </key>
<value>
<tuple>
<string>View</string>
</tuple>
</value>
</item>
<item>
<key> <string>portal_type</string> </key>
<value> <string>Action Information</string> </value>
</item>
<item>
<key> <string>priority</string> </key>
<value> <float>1.0</float> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string>View</string> </value>
</item>
<item>
<key> <string>visible</string> </key>
<value> <int>1</int> </value>
</item>
</dictionary>
</pickle>
</record>
<record id="2" aka="AAAAAAAAAAI=">
<pickle>
<global name="Expression" module="Products.CMFCore.Expression"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>text</string> </key>
<value> <string>string:${object_url}/Receipt_view</string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
# -*- coding: utf-8 -*-
"""
Extension to find receipts inside images.
Most of the functions is code from ocropy binaries modified
to work inside erp5 and adapt to receipt binaries and with more
explanation
https://github.com/tmbdev/ocropy
"""
import numpy as np
import scipy.ndimage as ndi
import StringIO
from matplotlib import pylab
import matplotlib.image as mpimg
import scipy.stats as stats
import re
import ocrolib
def getReceiptValue(self, image_data):
"""
Function called from an erp5 script through externalMethod
that take an image and its name and save its binarized
version in the image portal.
----------------------------
@args:
- self: object
Represent the erp5 object from which externalmethods or module
objects can be called
- image_data:
base64 representation of the image to analyse
@return:
- ret: float
Represent total value paid on the receipt
----------------------------
This function return the total value of the receipt in euros.
"""
image_as_string = StringIO.StringIO(image_data)
image_as_array = mpimg.imread(image_as_string, format = 'JPG')
line_list, cleared = getLinesFromPicture(image_as_array)
# Start the neural network
network, lnorm = initRnnModel()
value_list = []
tofind = r"(EUR)|€|(TOT)"
for _, line in enumerate(line_list):
binline = ocrolib.extract_masked(1 - cleared, line, 3, 3)
# Corner case: he dewarping function from the normalizer fail
# sometimes on empty lines. Can be corrected with better segmentation
try:
evaluate = getStringFromImage(binline, lnorm, network)
if re.search(tofind, evaluate.upper()):
number = re.findall(r"\d+[\.|,]\d\d", evaluate)
value_list += [float(char.replace(',', '.')) for char in number]
except ValueError:
pass
return round(max(value_list), 2)
def initRnnModel(model_name = "en-default.pyrnn"):
"""
This function load the neural network model from slapos backend
and initialise it.
----------------------------
@args:
- model_name: string, default: en-default.pyrnn
Id of the object in data_stream_module that contain the rnn model
@return:
- network: lstm object
Represent the trained neural net
- lnorm: method from lstm object
Represent the size of the lstm object. Is used to scale the objects
to recognize from original size to the average network object.
----------------------------
LSTM meaning: https://en.wikipedia.org/wiki/Long_short-term_memory
lnorm is extracted for clarity. This function initialize the neural net after
loading.
"""
network = ocrolib.load_object(model_name)
for node in network.walk():
if isinstance(node, ocrolib.lstm.LSTM):
node.allocate(5000)
lnorm = getattr(network, "lnorm", None)
return network, lnorm
def getLinesFromPicture(image_as_array):
"""
Function that take an colorized image in argument and return a
cleared binarized image and a list of lines
----------------------------
@args:
- image_as_array: 3d array
Represent a colored image
@return:
- lines: array list
Represent lines of text that will be extracted
from the image
- cleared:2D array
Represent binarized image cropped and cleaned,
from which we will extract text lines
----------------------------
This function find the text position of text line in an image
The neural network recognition only works on independant lines
of a binarized image.
Could be improved by making this function return each line as
independant picture
"""
grey_image = convertGreyscale(image_as_array)
flattened_image = imageTransformation(grey_image)
binarized_image = imageBinarization(flattened_image)
binary = 1 - binarized_image
cleaned, scale = removeObjects(binary)
angle = getEstimatedSkewAngle(cleaned, np.linspace(-4, 4, 24))
cleaned = ndi.interpolation.rotate(
cleaned, angle, mode='constant', reshape = 0
)
segmentation = getSegmentizedImage(cleaned, scale)
# Sort the labels from top to bottom
line_list = ocrolib.compute_lines(segmentation, scale)
order = ocrolib.reading_order([line.bounds for line in line_list])
sorted_lines = ocrolib.topsort(order)
# Renumber the labels with growing values
total_labels = np.amax(segmentation) + 1
renumber = np.zeros(total_labels, 'i')
for i, line in enumerate(sorted_lines):
renumber[line_list[line].label] = 0x010000 + (i + 1)
segmentation = renumber[segmentation]
line_list = [line_list[i] for i in sorted_lines]
return line_list, cleaned
def getStringFromImage(image, lnorm, network):
"""
Function that use a neural network to get a string of character from
an image that contain a line of text
------------------------------------
@args:
- image: 2D array
Represent a binarized image that contain a line of text
- lnorm: norm attribute of the neural network
Used to scale the letters from the image to the text of the
neural network
- network: neural network
Used to predict text
@return:
- pred: string
Text extracted from image
------------------------------------
Getting the text of each line allow the neural network not to be confused
with noise.
"""
temp = np.amax(image) - image
temp = temp * 1.0 / np.amax(temp)
lnorm.measure(temp)
image = lnorm.normalize(image, cval = np.amax(image))
image = ocrolib.lstm.prepare_line(image, 16)
return network.predictString(image)
def getLineSeed(binary, scale, bottom, top):
"""
Function that find "line seeds" inside a binarized image. The line
seeds are multiple boxes that surround text in a picture.
--------------------------------------
@args:
- binary: 2D array
Represent the image from which line seed will be extracted
- scale: double
Represent the size of the average object
- bottom: 2D array
Represent the bottom boundary of line seeds
- top: 2D array
Represent the top boundary of line seeds
--------------------------------------
The line seeds act as a "mask" that isolate lines from each other
to allow segmentation
"""
# Use scale as a int
vrange = int(scale)
threshold = 0.1
# Find the bottom boundary
bmarked = ndi.maximum_filter(
bottom == ndi.maximum_filter(bottom, (vrange, 0)), (2, 2))
bmarked = bmarked*(bottom > threshold * np.amax(bottom) * threshold)
# Find the top boundary
tmarked = ndi.maximum_filter(
top == ndi.maximum_filter(top,(vrange, 0)), (2, 2))
tmarked = tmarked * (top > threshold * np.amax(top) * threshold / 2)
tmarked = ndi.maximum_filter(tmarked, (1, 20))
# Create seeds
seeds = np.zeros(binary.shape, 'i')
line_spacing = vrange / 2
for x in range(bmarked.shape[1]):
transitions = sorted([(y, 1) for y in pylab.find(bmarked[ : , x])] \
+ [(y, 0) for y in pylab.find(tmarked[ : , x])])[:: -1]
transitions += [(0, 0)]
for i in range(len(transitions) - 1):
y0, s0 = transitions[i]
if s0 == 0:
continue
seeds[y0 - line_spacing : y0, x] = 1
y1, s1 = transitions[i + 1]
if s1 == 0 and (y0 - y1) < 5 * scale:
seeds[y1 : y0, x] = 1
return ndi.maximum_filter(seeds, (1, vrange))
def getSegmentizedImage(binary, scale):
"""
Function that give every pixel of every text character on a same line
the same value so it can be easily separated latter
This function find the upper and lower boundaries of text lines with
a filter using the first derivative of gaussian kernel to get
horizontal change of intensity and stretching it
----------------------------
@args:
binary: 2D array
Represent a clean, straight binarized image from witch we will
find the seeds
scale: double
Represent the size of the mean object
@return:
segmentation: 2D array
Represent a picture with different pixel value for each line
of text.
----------------------------
The segmentization give us independant text lines. Rotating the
picture to make lines more straight could improve result.
Some args could be added to play on the stretching of the blur or
the threeshold for finding the mask boundaries
"""
boxmap = ocrolib.compute_boxmap(binary, scale, threshold = (.25, 4))
cleaned_image = boxmap * binary
# Finding horizontals gradient
gradient = ndi.gaussian_filter(1.0 * cleaned_image, (0.5 * scale,
scale * 6), order = (1, 0))
gradient = ndi.uniform_filter(gradient,(1, 20))
# Find the bottom (whiter) and top (darker) zones from grad
bottom = ocrolib.norm_max((gradient < 0) * (-gradient))
top = ocrolib.norm_max((gradient > 0) * gradient)
seeds = getLineSeed(cleaned_image, scale, bottom, top)
# Label the seeds then group them by line inside boxmaps
seeds,_ = ocrolib.morph.label(seeds)
labels = ocrolib.morph.propagate_labels(boxmap, seeds, conflict = 1)
spread = ocrolib.morph.spread_labels(seeds, maxdist = (scale / 4))
labels = np.where(labels > 0, labels, spread * binary)
return labels * binary
def removeObjects(binarized):
"""
This function identify objects as contigunious spot of white on a
black image and remove the bigger and smaller ones.
---------------------------
@args:
binarized: 2D array
Represent the inversed color binarized picture from which
objects bigger than the mean will be taken from.
@return:
binarized: 2D array
"cleaned" array
scale: double
Represent the size of the mean object
---------------------------
This function improve line detection and "clean" the image.
A coefficient could be added to the function to change the value of
height and width of the deleted object, or to choose only horizontal
or vertical objects.
"""
labels, n = ocrolib.morph.label(binarized)
objects = ocrolib.morph.find_objects(labels)
# Calculate the median of all objects
bysize = sorted(objects, key = ocrolib.sl.area)
scalemap = np.zeros(binarized.shape)
for sub_object in bysize:
if np.amax(scalemap[sub_object]) > 0:
continue
scalemap[sub_object] = ocrolib.sl.area(sub_object) ** 0.5
scale = ndi.median(scalemap[(scalemap > 3) & (scalemap < 100)])
# Take the mesurement of small objects
sums = ndi.measurements.sum(binarized, labels, range(n + 1))
sums = sums[labels]
# Find all objects and remove big ones
for i, b in enumerate(objects):
if (ocrolib.sl.width(b) > scale * 8) \
or (ocrolib.sl.height(b) > scale * 8):
labels[b][labels[b] == i + 1] = 0
# Recreate an array without big objects
binarized = np.array(labels != 0, 'B')
# Remove small objects from this image
binarized = np.minimum(binarized, 1 - (sums > 0) * (sums < scale))
return binarized, scale
def getImageWhitelevel(image):
"""
Function that help flatten the image by estimating locals
whitelevels. This remove local extremes and give an image with
homogenous background and no details
------------------------------
@args:
- image: 2D array
Represent a greyscale image
@return:
- white_image: 2D array
Represent a greyscale image with no local extreme
------------------------------
This function result will be substracted from the original image
to make that only local extremes stand out.
"""
white_image = ndi.filters.percentile_filter(image, 50, size = (80, 2))
white_image = ndi.filters.percentile_filter(white_image, 50, size = (2, 80))
return white_image
def getEstimatedSkewAngle(image, angle_list):
"""
Function that estimate at which angle the image is the most
straight
-------------------------------
@args:
- image: 2d array
Represent the greyscale original image
- angle_list: float list
Represent the list of angles that will be tested in the
function
@return:
a: float
Represent the value of the angle (in degrees) for which the
image is the less skew
-------------------------------
This function is needed to get a better segmentation (since the
segmentation is done horizontally). It calculate the mean of each
line in the picture then calculate the variance, and return the
angle value for which the variance is the highest.
"""
estimates = []
for angle in angle_list:
mean = np.mean(ndi.interpolation.rotate(
image, angle, order = 0, mode = 'constant'), axis = 1)
variance = np.var(mean)
estimates.append((variance, angle))
_, angle = max(estimates)
return angle
def imageTransformation(grey):
"""
Function that perform cropping and flattening -- Removing
homogenous background and small extremes-- on an image.
-------------------------------
@args:
grey: 2D array
Represent the original greyscale image
@return:
flat: 2D array
Represent the original image cropped and flattened
-------------------------------
This function reduce the size of the image and clear noise from
homogenous background
"""
# Reduce extreme differences in the greyscale image
image = grey - pylab.amin(grey)
image /= pylab.amax(image)
white_image = getImageWhitelevel(image)
# Get the difference between the whiteleveled image and the
# original one and put them betewwn 0 an 1
flat = np.clip(image - white_image + 1, 0, 1)
# Calculate coordinate to crop the image, can be done in another
# function to improve readability
mask = ndi.gaussian_filter(
flat, 7.0) < 0.9 * np.amax(flat)
coords = np.argwhere(mask)
# Bounding box of kept pixels.
x_min, y_min = coords.min(axis = 0)
x_max, y_max = coords.max(axis = 0)
return flat[x_min - 10 : x_max + 10, y_min - 10 : y_max + 10]
def imageBinarization(flattened_image):
"""
Fontion that take a flattened image and binarize it to make OCR
easier
-------------------------------
@args:
- flattened_image: 2D array
Represent a greyscale image
@return:
- binarized_image: 2D array
Represent a binarized image
-------------------------------
Binarized image is needed to get text boundaries and make text
easier to distinguish from background.
"""
gaussian_image = ndi.gaussian_filter(flattened_image, 1)
# Applying a Gaussian filter with a small sigma and substracting
# it from the "original" image allow to do an approximation of
# Laplacian.
blurred_gaussian = ndi.gaussian_filter(gaussian_image, 3)
# Add the scaled Laplacian to the smoothed image to sharpen its edges
sharpened = 1.5 * gaussian_image - blurred_gaussian
# Estimate low and high threshold
low = stats.scoreatpercentile(sharpened.ravel(), 5)
high = stats.scoreatpercentile(sharpened.ravel(), 70)
# rescale the image to enhance differences
sharpened = (sharpened - low) / (high - low)
sharpened = np.clip(sharpened, 0, 1)
# Binarization
return sharpened > 0.7
def convertGreyscale(image):
"""
Function that take a three dimension numpy array
representing a RGB picture and return a two dimension
numpy array of this picture in greyscale with values between
0 and 1
@args:
- image: 3 dimension numpy array from a RGB picture with
values between 0 and 255
@return:
grey: 2 dimension numpy array with values between 0 and 1
The greyscale conversion is needed to detect edge in
the preprocessing and later to extract easier to process data
"""
return image.astype(float).sum(axis = -1) / 3. / 255
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="Extension Component" module="erp5.portal_type"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>_recorded_property_dict</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAI=</string> </persistent>
</value>
</item>
<item>
<key> <string>default_reference</string> </key>
<value> <string>ReceiptRecognition</string> </value>
</item>
<item>
<key> <string>description</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>extension.erp5.ReceiptRecognition</string> </value>
</item>
<item>
<key> <string>portal_type</string> </key>
<value> <string>Extension Component</string> </value>
</item>
<item>
<key> <string>sid</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>text_content_error_message</string> </key>
<value>
<tuple/>
</value>
</item>
<item>
<key> <string>text_content_warning_message</string> </key>
<value>
<tuple>
<string>W:243, 2: Attempting to unpack a non-sequence defined at line 181 of scipy.ndimage.measurements (unpacking-non-sequence)</string>
<string>W:272, 2: Attempting to unpack a non-sequence defined at line 181 of scipy.ndimage.measurements (unpacking-non-sequence)</string>
</tuple>
</value>
</item>
<item>
<key> <string>version</string> </key>
<value> <string>erp5</string> </value>
</item>
<item>
<key> <string>workflow_history</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAM=</string> </persistent>
</value>
</item>
</dictionary>
</pickle>
</record>
<record id="2" aka="AAAAAAAAAAI=">
<pickle>
<global name="PersistentMapping" module="Persistence.mapping"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>data</string> </key>
<value>
<dictionary/>
</value>
</item>
</dictionary>
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<record id="3" aka="AAAAAAAAAAM=">
<pickle>
<global name="PersistentMapping" module="Persistence.mapping"/>
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<pickle>
<dictionary>
<item>
<key> <string>data</string> </key>
<value>
<dictionary>
<item>
<key> <string>component_validation_workflow</string> </key>
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<persistent> <string encoding="base64">AAAAAAAAAAQ=</string> </persistent>
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<name>Access Transient Objects</name>
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<role>Assignor</role>
<role>Associate</role>
<role>Auditor</role>
<role>Author</role>
<role>Manager</role>
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<permission type='tuple'>
<name>Access contents information</name>
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<role>Assignor</role>
<role>Associate</role>
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<role>Author</role>
<role>Manager</role>
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<permission type='tuple'>
<name>Access session data</name>
<role>Assignee</role>
<role>Assignor</role>
<role>Associate</role>
<role>Auditor</role>
<role>Author</role>
<role>Manager</role>
</permission>
<permission type='tuple'>
<name>Add portal content</name>
<role>Assignor</role>
<role>Author</role>
<role>Manager</role>
</permission>
<permission type='tuple'>
<name>Add portal folders</name>
<role>Assignor</role>
<role>Author</role>
<role>Manager</role>
</permission>
<permission type='tuple'>
<name>Change local roles</name>
<role>Assignor</role>
<role>Manager</role>
</permission>
<permission type='tuple'>
<name>Copy or Move</name>
<role>Assignee</role>
<role>Assignor</role>
<role>Associate</role>
<role>Auditor</role>
<role>Author</role>
<role>Manager</role>
</permission>
<permission type='tuple'>
<name>Delete objects</name>
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<role>Manager</role>
</permission>
<permission type='tuple'>
<name>List folder contents</name>
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<role>Assignor</role>
<role>Associate</role>
<role>Auditor</role>
<role>Author</role>
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</permission>
<permission type='tuple'>
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<role>Manager</role>
</permission>
</permission_list>
<portal_type>Receipt Recognition Module</portal_type>
<title>Receipt Recognition</title>
</module>
\ No newline at end of file
<allowed_content_type_list>
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<item>Receipt</item>
</portal_type>
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\ No newline at end of file
<base_category_list>
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<item>business_application</item>
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</base_category_list>
\ No newline at end of file
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\ No newline at end of file
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image = context.getFollowUpValue()
if image is not None:
try:
total = container.ReceiptRecognition_getReceiptValue(image.getData())
msg = "Total found"
context.edit(
total = total,
)
except ValueError as e:
msg = "Could not find value, please submit it manually"
else:
msg = "Cannot find the image"
if batch_mode:
return
context.Base_redirect(
'view', keep_items = dict(portal_status_message=msg, my_source="test"))
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<?xml version="1.0"?>
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<item>
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</item>
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<key> <string>update_action_title</string> </key>
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</pickle>
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<?xml version="1.0"?>
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</item>
<item>
<key> <string>form_id</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</value>
</item>
<item>
<key> <string>tales</string> </key>
<value>
<dictionary>
<item>
<key> <string>field_id</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>form_id</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</value>
</item>
<item>
<key> <string>values</string> </key>
<value>
<dictionary>
<item>
<key> <string>field_id</string> </key>
<value> <string>my_relation_field</string> </value>
</item>
<item>
<key> <string>form_id</string> </key>
<value> <string>Base_viewFieldLibrary</string> </value>
</item>
<item>
<key> <string>portal_type</string> </key>
<value>
<list>
<tuple>
<string>Image</string>
<string>Image</string>
</tuple>
</list>
</value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string>Follow Up</string> </value>
</item>
</dictionary>
</value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="ProxyField" module="Products.ERP5Form.ProxyField"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>delegated_list</string> </key>
<value>
<list>
<string>title</string>
</list>
</value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>my_title</string> </value>
</item>
<item>
<key> <string>message_values</string> </key>
<value>
<dictionary>
<item>
<key> <string>external_validator_failed</string> </key>
<value> <string>The input failed the external validator.</string> </value>
</item>
</dictionary>
</value>
</item>
<item>
<key> <string>overrides</string> </key>
<value>
<dictionary>
<item>
<key> <string>field_id</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>form_id</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</value>
</item>
<item>
<key> <string>tales</string> </key>
<value>
<dictionary>
<item>
<key> <string>field_id</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>form_id</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</value>
</item>
<item>
<key> <string>values</string> </key>
<value>
<dictionary>
<item>
<key> <string>field_id</string> </key>
<value> <string>my_string_field</string> </value>
</item>
<item>
<key> <string>form_id</string> </key>
<value> <string>Base_viewFieldLibrary</string> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string>Title</string> </value>
</item>
</dictionary>
</value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="ProxyField" module="Products.ERP5Form.ProxyField"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>delegated_list</string> </key>
<value>
<list>
<string>title</string>
</list>
</value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>my_total</string> </value>
</item>
<item>
<key> <string>message_values</string> </key>
<value>
<dictionary>
<item>
<key> <string>external_validator_failed</string> </key>
<value> <string>The input failed the external validator.</string> </value>
</item>
</dictionary>
</value>
</item>
<item>
<key> <string>overrides</string> </key>
<value>
<dictionary>
<item>
<key> <string>field_id</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>form_id</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</value>
</item>
<item>
<key> <string>tales</string> </key>
<value>
<dictionary>
<item>
<key> <string>field_id</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>form_id</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</value>
</item>
<item>
<key> <string>values</string> </key>
<value>
<dictionary>
<item>
<key> <string>field_id</string> </key>
<value> <string>my_string_field</string> </value>
</item>
<item>
<key> <string>form_id</string> </key>
<value> <string>Base_viewFieldLibrary</string> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string>Total</string> </value>
</item>
</dictionary>
</value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
erp5_dms
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GPL
\ No newline at end of file
Receipt Recognition Module | view
Receipt | receipt_convert
Receipt | view
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extension.erp5.ReceiptRecognition
\ No newline at end of file
receipt_recognition_module
\ No newline at end of file
Receipt Recognition Module | Receipt
\ No newline at end of file
Receipt Recognition Module | business_application
\ No newline at end of file
Receipt
Receipt Recognition Module
\ No newline at end of file
erp5_receipt_recognition
\ No newline at end of file
erp5_full_text_mroonga_catalog
\ No newline at end of file
erp5_receipt_recognition
\ No newline at end of file
0.1.0
\ No newline at end of file
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