PeterPan1990

import caffe_pb2 as proto
import leveldb
import sys

db = leveldb.LevelDB('./cifar_test',block_size=40 * (2 << 20) )

import struct

def unpickle(file):
    import cPickle

PeterPan1990

Deep Learning of Binary Hash Codes for Fast Image Retrieval

name: Deep Binary Hash Codes CIFAR10

caffemodel: KevinNet_CIFAR10_48.caffemodel

caffemodel_url: https://www.dropbox.com/s/1om7xa8mz93wkzh/KevinNet_CIFAR10_48.caffemodel?d

gist_id: 266d4150a1db5810398e

PeterPan1990

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