Steps for installing Tesseract(4.0) on AWS-Lambda

AWS-pytesseract.md

Tesseract-OCR AWS Setup (using pytesseract and python2.7)

Follow these steps on a fresh 64bit AWS AIM instance

Connect to AWS AIM instance, then follow these steps:

1. Update system libraries

sudo yum -y update
sudo yum -y upgrade

2.Compile Leptonica

cd ~
sudo yum install clang -y
sudo yum install libpng-devel libtiff-devel zlib-devel libwebp-devel libjpeg-turbo-devel -y
wget https://github.com/DanBloomberg/leptonica/releases/download/1.75.1/leptonica-1.75.1.tar.gz
tar -xzvf leptonica-1.75.1.tar.gz
cd leptonica-1.75.1
./configure && make && sudo make install

3.Compile autoconf-archive

cd ~
wget http://mirror.squ.edu.om/gnu/autoconf-archive/autoconf-archive-2017.09.28.tar.xz
tar -xvf autoconf-archive-2017.09.28.tar.xz
cd autoconf-archive-2017.09.28
./configure && make && sudo make install
sudo cp m4/* /usr/share/aclocal/

4.Compile tesseract

cd ~
sudo yum install git-core libtool pkgconfig -y
git clone --depth 1  https://github.com/tesseract-ocr/tesseract.git tesseract-ocr
cd tesseract-ocr
export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig
./autogen.sh
./configure
make
sudo make install

5.Install Python packages

cd ~
virtualenv ~/tfenv
source ~/tfenv/bin/activate

pip install pillow
pip install cython
pip install opencv-python==3.4.2.16
pip install tesserocr
pip install pytesseract

6.Copy Files

cd ~
mkdir tesseract-aws
cd tesseract-aws
cp /usr/local/bin/tesseract .
mkdir lib
cp /usr/local/lib/libtesseract.so.5 lib/
cp /usr/local/lib/liblept.so.5 lib/
cp /usr/lib64/libjpeg.so.62 lib/
cp /usr/lib64/libwebp.so.4 lib/
cp /usr/lib64/libstdc++.so.6 lib/
cp -r ~/tfenv/lib/python2.7/site-packages/* .
cp -r ~/tfenv/lib64/python2.7/site-packages/* .
mkdir tessdata
cd tessdata
wget https://github.com/tesseract-ocr/tessdata_fast/raw/master/osd.traineddata
wget https://github.com/tesseract-ocr/tessdata_fast/raw/master/eng.traineddata
mkdir configs
cp /usr/local/share/tessdata/configs/pdf configs/
cp /usr/local/share/tessdata/pdf.ttf .
cd ..

7.Create a python file for testing

vim lambda_function.py

see the file below and set the path

import pytesseract
import PIL.Image
import io
from base64 import b64decode

def lambda_handler(event, context):
  pytesseract.pytesseract.tesseract_cmd = "/var/task/tesseract"
  binary = b64decode(event['image64'])
  image = PIL.Image.open(io.BytesIO(binary))
  text = str(pytesseract.image_to_string(image))
  return {'text' : text}

8.Create a zip file

zip -r ~/tesseract-aws.zip *

9. Download the zip file from S3 to local machine

scp -i key.pem ec2-user@AWS_EC2_INSTANCE_IP:/home/ec2-user/tesseract-aws/tesseract-aws.zip .

10. Upload this file to an EC2 instance and then use its url in Lambda

11.ADD Environment variable in Lambda with

Key TESSDATA_PREFIX

Value /var/task/tessdata

12. For Testing set the Handler to: lambda_function.lambda_handler

13. Test it using a json like this

{
"image64": "iVBORw0KGgoAAAANSUhEUgAAArsAAAJoCAYAAABmySkDAAAAiXpUWHRSYXcgcHJvZmlsZSB0eXBlIGV4aWYAAHjaVY7BDcQwCAT/VJESMOAFyrlEiXQdXPnBck5W5gGrlT2Czt/3om3QWMi6BxLgwtJSPhWCJ8rchNvYNSfP1lZJVk0qMyDD2dZDe/o/XRG43NzRceCQssupolGz/tGw8jgjl2TfeWr03SP4pacbUogsi2Nj5pQAAAoGaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8P3hwYWNrZXQgYmVnaW49Iu+7vyIgaWQ9Ilc1TTBNcENlaGlIenJlU3pOVGN6a2M5ZCI/Pgo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA0LjQuMC1FeGl2MiI+CiA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyIKICAgIHhtbG5zOnRpZmY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vdGlmZi8xLjAvIgogICBleGlmOlBpeGVsWERpbWVuc2lvbj0iNjk5IgogICBleGlmOlBpeGVsWURpbWVuc2lvbj0iNjE2IgogICB0aWZmOkltYWdlV2lkdGg9IjY5OSIKICAgdGlmZjpJbWFnZUhlaWdodD0iNjE2IgogICB0aWZmOk9yaWVudGF0aW9uPSIxIi8+CiA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgCjw/eHBhY2tldCBlbmQ9InciPz66DhFUAAAABHNCSVQICAgIfAhkiAAAIABJREFUeNrt3We0leWB9+H/PoUO0hEQRJqADRt2Y40aS1SkKInJGE2PaSaTSZnElEmbzJiYTGI0iSN6wIa9RY0asaGiKEpREGw0AekcTtnvB/POmgRm5mwDePbhutbaK/mQ4nnq77n3/dy7UCwWiwEAgBaowiYAAEDsAgCA2AUAALELAABiFwAAxC4AAIhdAADELgAAiF0AABC7AAAgdgEAQOwCAIDYBQBA7AIAgNgFAACxCwAAYhcAAMQuAACIXQAAxC4AAIhdAAAQuwAAIHYBAEDsAgCA2AUAALELAIDYBQAAsQsAAGIXAADELgAAiF0AABC7AACIXQAAELsAACB2AQBA7AIAgNgFAACxCwCA2AUAALELAABiFwAAxC4AAIhdAAAQuwAAIHYBABC7AAAgdgEAQOwCAIDYBQAAsQsAAGIXAACxCwAAYhcAAMQuAACIXQAAELsAACB2AQAQuwAAIHYBAEDsAgCA2AUAALELAABiFwAAsQsAAGIXAADELgAAiF0AABC7AAAgdgEAQOwCACB2AQBA7AIAgNgFAACxCwAAYhcAAMQuAABiFwAAxC4AAIhdAAAQuwAAIHYBAEDsAgAgdgEAQOwCAIDYBQAAsQsAAGIXAADELgAAiF0AAMQuAACIXQAAELsAACB2AQBA7AIAgNgFAEDsAgCA2AUAALELAABiFwAAxC4AAIhdAADELgAAiF0AABC7AAAgdgEAQOwCAIDYBQBA7AIAgNgFAACxCwAAYhcAAMQuAACIXQAAELsAAIhdAAAQuwAAIHYBAEDsAgCA2AUAALELAIDYBQAAsQsAAGIXAADELgAAiF0AABC7AACIXQAAELsAACB2AQBA7AIAgNgFAACxCwAAYhcAALELAABiFwAAxC4AAIhdAAAQuwAAIHYBABC7AAAgdgEAQOwCAIDYBQAAsQsAAGIXAACxCwAAYhcAAMQuAACIXQAAELsAACB2AQBA7AIAIHYBAEDsAgCA2AUAALELAABiFwAAxC4AAGIXAADELgAAiF0AABC7AAAgdgEAQOwCACB2AQBA7AIAgNgFAACxCwAAYhcAAMQuAABiFwAAxC4AAIhdAAAQuwAAIHYBAEDsAgCA2AUAQOwCAIDYBQAAsQsAAGIXAADELgAAiF0AAMQuAACIXQAAELsAACB2AQBA7AIAgNgFAEDsAgCA2AUAALELAABiFwAAxC4AAIhdAAAQuwAAiF0AABC7AAAgdgEAQOwCAIDYBQAAsQsAgNgFAACxCwAAYhcAAMQuAACIXQAAELsAAIhdAAAQuwAAIHYBAEDsAgCA2AUAALELAIDYBQAAsQsAAGIXAADELgAAiF0AABC7AAAgdgEAELsAACB2AQBA7AIAgNgFAACxCwAAYhcAALELAABiFwAAxC4AAIhdAAAQuwAAIHYBABC7AAAgdgEAQOwCAIDYBQAAsQsAAGIXAADELgAAYhcAAMQuAACIXQAAELsAACB2AQBA7AIAIHYBAEDsAgCA2AUAALELAABiFwAAxC4AAGIXAADELgAAiF0AABC7AAAgdgEAQOwCACB2bQIAAMQuAACIXQAAELsAACB2AQBA7AIAgNgFAEDsAgCA2AUAALELAABiFwAAxC4AAIhdAADELgAAtBBVNgHA1tCQdYvn5cUXZ+fl+QuyYMGCvPrG4ixZuiRLl63I26vXZO3atVm3oTZ1dfWpa2hMoaIylVVVqaqqSqs27dK+fYe0b98+HTt1TpeuXdOtW7d07dY9PXfund69e6dP7z7ps0v/7LpLz3SotsUBxG5LUjctX9vrsPx4Tn3T/vOFjjn1Dy/llo/0SsHW+yv1M36fi34zLRuLzewfrFBIRaEiFZVVqapuldZt2qRd+w7p0LFTOnfpmm7de6TXzn3SZ5d+6dujvZP3Pda47s0898if8uDUx/LEE9Py5PSZeWX5xjRuh+OqUNU+3fvumgG7DcygIUMzdOjQ7L777hk2fESGDeyZdhXO0213mlakorIyVVVVqapqldbt2qdD+/Zp37Fzunbvlu7duqfHzn3St2/f9Orc2tenLeHavM1VpPuxX8rFYwan0mGwbc7bYrFYtBmav9qpX8yIoy7J/IYm79q0PeaXmX3vp9Pf1favt+XNE9LjzJqsKdsjv5Cq9t3Tf/DuGTZ8j+y1z77Z78CDcsiovdKvo0vltlPMhjeeyj0335Sbbr41d019MW9tLKbYzI6NyvY9M3D4ntlr772zz8h9s99++2f/kbund/tK5+l2fnitbt8z/QcNyZChQzNsxJ7Ze5+RGbnvvtlzQOcYmG+J1+Z3qzIDvvBg5v774Y6LbcTgUFnYkKk1N2ZhQyn/nWI2Tp2Ya+d9Il8ZIoBaWnTVr1uW+TOWZf6Mqblz8l/urdVdMvCAo3Liyafl9NEfzFHDujjBt4a6ZXnmlt/nsiuuzHX3z8nK+mKzPjYa1i3JS08tyUtP3Z8p/7+7qjqk/3mT8sJlp6S9PbqddkUxdWuXZN6MJZk3Y2ru/q8Irkz7nYfnwMOOyPuOOS7vP/6YjBrc2bkK25Axv3Kw7oHU3PRGGkr8rxU3PZWrr3kh9bbgXx/0PYfnwD13Tbe2lS1qikexbmXmPXZTfvXNf8jxI/qk/6jx+caVj+aNWvv8XVk/P/f8+ydy1JAB2X/M13LZPbObeej+L8dG/dosfv2t1Badp+/9zmjIukUz8+ANv87Fnx6dw4b2ys57vD/n/fNvc/eslSVf512b4f9mGkMZWH3ThzLorGvyVmPp/92qoV/Jw8//JAe3sh0307gxy197OXPnzMncuXMzZ+7c//r3L7+2IhsaWsKpUUjbXY/Ox7/9k3zr3P3TzSD//61+aR7/3bdz0cW/z6OLNr2raQqF6p3Sb8S+2W/ffbLHsCEZMmi39OvbO717dU+XnTqlY/s2aVVdnao0pHbjhmzcuDHrVi3P0iWLs3jx4rz56suZM3t2Zs+alZnPv5AFKzfl771St37/b7Pw7gvSq+A8bb6na9vscvDp+egnPptPjj80fVu7Nrf4fZ7ENIbt8ZBJ89a4vDjprC7FQlLMu/lU9i9++v4NtmOJGjYsLy54/uHiz8/oVax4t9u+OX0KVcVeR1xUvGneRjv3f97rxeXTfl38h327FCsLpW7fimL7fgcVz/zcD4tX3v108dU19VvxGrC+uOTFqcVbfv+T4lfOPaG4T++2xUKh9GOg1dG/LL7W4Dwtj0+h2Lr3IcV/+Nc7iy+vdWa2/H1eWRzwhYeLm+zebcbIbnN/GFl6VU4f8tHcuvrd7qaK9ProLXn5D6ekg81ZorpM/eLQHHXJgiZ+tViVfb7zdJ769t4lzr8rprGhIXW1G7Jh3dqsWrUiy5ctyaLXX82C+S9nzqyZee6ZpzP9hTey5u8a0Sikotsh+cdJU/L943uZw/RXu/r13P3dj+ZjP/5T3qxr+jYuVHXLnqd+LJ/7zPkZd/SQdNoeG7VxbRZOuztTrp2UmmvvzNOLNjZp9LnV4f+eOQ99IQMqnKelnqfFhvrU19eldsParFmzJqtWvpVlS5Zm8ZuvZuGCBZn30uzMevGFzHxxQVbUNm7FlxYLadX7sFzwvX/PxR89wDcz2/3a/F+DgkmxMY2NjWloqE99fUPq6+tSt2lTamtrs6m2Nhs3bsiG9euzbu2arF6zOqtXvZ23V67MihXL89bSJVmyZHHefOP1zJ89K6+tqv+bY8TI7rZmTnzzTt0suqUm9/9dr6Y2ZtmtV+XOFSdnbFezoJqnQioqq9K6Xce0btcxnXv0zq6D99j88v72y5l27x25+YZJmXTbtLyxoVjy8dS4/NH86IPH5u3r7s0vT+kteJM0vPnH/PM5H86P/7w0TX2WKFTslBGjL8r3Lr4wpw/vtH3nF1Z0yK4Hn5UvHnxWvviTtzLjtt/n0p//KjUPv5r/7ZAo1tWlztDGuztDK6tSXVmV6tZt06Fzj/TuNzDDtrSNa1dk/nPT8uifH8gDf7o39zw4I4vW/z3xW8ymRVPzqwsOyXVXfCL/dvkPM2HPjuazbu/9XygkhcpUVlSmsqo6rd719JKGzP7RIdn7n55Mnc26XbnXNWeNr2ZKzUNZX9zszHvn5Gvq/8zKu3PVLUviPlfeqjsPzmFjPp+fXvt4XlkwLVf/0ykZ3L70215xwwu57ENj8uPpG3b4h8m1z/5Hxh52Wn74UFNDt5B2Q87Mj+59IdOv+2bO2N6hu9lB0T37nPnVXPHQ3Lz858vyuaP7pc3/9A9UX+cGu62jqHXXDDrwxHz4yz/O7++YnteWLshj1/8sF35wZHq2/juOlGJ9lj3+q5w7at+c9e+PZ0WjbV2eKtN/yMD/+RxF7O6IGuZfn5pHazeL1EL74/Pxcwc3fVi+uDZ/mnhjXnOBbDnh2/OATPiX2/Lcs9fn86O6lHwiN656NBef++1MXbfjhu7bj/4gpxz3uUxZUNu0B8FC+wz/8OV5/Okb8tVj+qZ5vfPZOn0O/3h+8adZmXHDhTmgw+Z302LdJiO72/sG275fDjrrS/n5zc9k4bxHMvGfx2Vk96p3/YBU3DAvU758dEaN+488s8bOLEetdhuS/qajiF3+K3Uz59rJeWqzu1MhnY4/N9/5/NkZ3vTazcZHJmbyyw02awvTdvDoXPKnh3LpqX1SWdIdtJjaFy/N5382I5t2wNBd/fj3csrJ385Dy5v2BFio7JVjf3BvHvnPj2Wvjs15WKZ9hp7+uYzbawsXh7o6yxC+h9r0PSQfunhyps9/PlO+e1ZGdKp4d9Fb3Jh5N3wuRx97Ue560zW93FTtNiQDqwztil3eUf98Jl87Y/ORmIrOOWH8B9Jr77Nzzr7VTb5YFjc9nauvmelm1xK13yufrrk53xrVobSbZ3Fjnv3FD3LTWzvWCFHtrF9n/AcvziNvNzV0e+cDv7gvt/3TIelSDveoin7ZY/hOm13cjew2D4WOw3L6t67Ps7Puy0/GDs+7e3ZqzKon/z2jj/9UbnrNVb2sdBiUwb2ll9glSVI3fVKum7X5Rayi6wdy9oldUqgcmrFnH5RWTb5Q1ufFSVfnyU22bcu8gB6Yr//+n3NoiXN4G1fcll9PWpgdZYZLcfm9+cpZX8rdS5v4F1fslMO+e1uu/fSeaVs2f2V1hu25++bTnMzZbV57qc/Ruejap/LU5AtzcLd38yMKxWx48YpM+MAXc88yTzFlo2q3DB1obQCxS5LaPF5zfV6u33x39Tz17BzX6Z1/P2DMhBzRtumXyIb512biwxts3pZ68xzxmfzgYwNT0nSwYm0eu+6mLNgRarfxtVzz8XPzqxebOke3Mv3PviLXfm3/MvuJ3Yr03WNEuvzN1d1qDM1Ruwwd+/M88HhNPrFvp3dxQy5mw8z/yPizfppnXdrLQ6F7Bg3uIr7ELtnwcGqmvLr5+oEVvXPa+GP+a73cir5nZMIxJXx13fBGpky8L2ts4RZ74zz8U+dnv+pSxoiKqXv6j3mgxY8MNeTly87P529e3MRR7EKqh3wqv/vl6PQpw6tk1e57Zve/HTwyZ7fZajN4bP7jgfvykxN7p/TpnI15++FvZdyX7spbHmbK4ezMwCG7WfdV7LL2TzW5+Y3NXzyo3OX0jD/yv32ZWuiZ0yackM5Nvjg2ZumtV+WOFa6ILVXlkNEZs1+JP2lR+3Qee6Zlz29peOV3+dw37mvykk2FqkG54Fffz7Gdy/NFkoreIzKi219f3ov1Rnabs8JOB+bLN92fX57at/TgLW7K3MvPz2evezMW3Wn+2dV3yKC0846a2N2xrcq9k27L5lMKK7Pr6PE5rM1fXR7T9QMT8oHuTd+NxbfvzsSbrbnbcmt3QI44YtfSpjI0rsrsF99ouTfJ4qJMvuib+ePKpv6FFek19ke5+Nidynfx/qph2XPY3zz01G0yZ7e5azM8n5h8Z358TLfSb84Nb+b6Cz+XmjfkbnNXPXBoBhjaFbs7suKKu1Jzx/LNw6NqcM4af9Dma3t2Oj4f+uDOTd+RxXX501XXZ6HrYUu9jGb4yD1KeHExSRry+sLX01IXMVrzwA/yz7csa3LMF9odlq9+5/R0L+eRl4peGTGi519fF4zsloe2e+eL19bkU8Nbl/yw1bj0lnzt67eaztDcxyR2HZKBrQztit0dN3Wz7Laa3LNq8ytV1e5jMn6/Lf1qdvscNeHM9Kts+v9H7WNXZ/Jc6zO2VO36D0ivks7sxqxcvrJljuw2vJTL//n3eaXJh3tl+p7zT/nYkHJf9b0qw/bcPf/9ilEs1qXOpN2yUOj2/vxrzXdyaMnrkjXkzZqv5UePeluteT/QDMyQXfyyhNjdUTW+mZtr7s/azVq3OnuOHZu9/4evPdocek7GDGz6iVPcND3X1DznZZWWeqPcqXM6lfgDE5s2bmyRI7tr7v9Zfv74hiZP2ylU75XzP3d8OpX/UZDuw0ek13+/LBTrU2ceQ9loM/LLueK778tOJfZusX5ufvvtP+QV3941X5UDM2Q3sbt9H/9pPq376pTU/HnzG3OhemTGjR3xP8/DbHVgzhk3PJd8v6k/GlGfWZOuybRv7ptDW9nuLS5227Qt8bfXC6morGx5T77FN3Pdz2vyWkPTt0PbIz+e8/ZsGZfFqn3Ozre+0SkL/v/fXzU0e7Z2fpSP6gz77K/yjetH5R8fXVfCexbFrHnw3/Lzqf+QS45sazM2y4t0lxwy4cJ8ctfVf9mvFel+6M5GH8XujqAh866vyWO1m/88cPWB43LW0Mr/dTfuffb47PPjb+XpJk7Ka3jl2kz883dz6HHtbPqWZtOmEn8CuJC27du1uAttw+z/zGX3rW16JBQ65NgPj84uLWRDFDofkvMvPsT5UNZ36BH57M8+kyuP/GleLGXCdcMrueqSKfn6ERPS09TQZqgyIz70k/z6Q7bE9uJBovncmXPt5KezabPWbZ1Dxp+Vgf/HnqocNj7njGpVwpq7b2bKVfdmtS3f4hQ3rM/GYmmXgV59WtqoQn2emTgxz2xq+oYodDw2Y0/pEW1Ac9L2oC/nW6d3L/H8bMzbd1+eyd5EBrHbrG7Nz12ba5/bfEJdoe3hGX9Gv/97R1XsljHnHFbC19eNWXbbxNyx3Gu7LU3jssVZWso9rlCVAYP6p0XNINs0LZOum1vCvPRC2h95ek7oKnVpZgo9c+ZXz8+wEr+HLW58NP959ax4FRnEbjNRl6cnX5vZ9Vu4AR91dk7vW9GkXbnLmRNydPum36yLq+7JxJsWW3O3RSlmxWuvZ10psVs5OPuP7NyiRjTrnp6SWxeUcJsvtMqBJx6TblqXZqjVfp/MZ97XrrRztFiX52+ckllqF8Rus1D7WGqun7f5KFShY44df2p6NfEKV+j1wUx4fwkL4RfX5YGrrbnb0h6cXnxuVkk/HlDR64i8b3hLmr5fn+duvyultG6q9sgxR/VxQaSZ3qn7Zex5J5a8MkPdCzfnltlqF1zbm4ENf67JlFc3vyAVdnp/xp/cvenxWuiWkyd8IN2a/gsTqX3s6kyy5m7L0bAgTz61tIQ1cyvS66QP5pA2LWkbzMt9971U0tJ6FT0PzqFDLAVEc1VI91Mm5ANdS7xl18/MPfe95ieEEbs2wXttbe6vuTlvNmy+a7p+4OycVOIcwp1OmJDTepbw88Gbnsk118yw5m4L0fj6PblrRgl7s2pQJpx3VFrSmhzFt6bmwedLOaILaXPAIdm32vFDM9bp2Jx5fOfSbtrFujx934NZYa4aYpf31Kp7UnPbFn7KtKJHTjn7/dmp1P+99kfnnDP7lvCyUX1mT7o6T2yyK1pA6uaVm67LE01egaCQDod/Np88qGUtvlr71MN5sraUu3tlBo/cOx3N16V5126OOunwtC3xB2M2PvlInnZ9R+zy3ilm+Z2TcteKzb9kqtj51Jx9TId38b/ZNkecMyYDSvhGtmHB9bnqwfV2R7nb+ER+85vHm7zsWKF6aD7+vfMzqEVdBeoz58lnsqqk1SjaZY99Blt0nGaukC5HHZcDSvwGonH5k3l8jqlq2/xuvnJiTmtfkUKh8M6nzYm5YqkhdbFLUlya22r+mFWbnQ+V6Xv62Xnfu/xuudVB52Tc7iXcuhvezE0T/2jN3bLWkJd++41cNreJX98XqjLogkvzrcNa2I+KFFdnxvSXSltuqWpghg/x02KUwQ27z0E5eLcSH8saXsqzz6+18bb1Y/YL0zOzVtyKXTZ/4n7z5tT8aQu/8FTZP2eOPyzv+p2hqpE5++y90vQBgMa8dfvE3P6WE7VcbXrxl/nUxQ9lTZN2YSHt9v1Krvzhcenc0r66b3gpL8ypK2k5vUL1wAwZ4OU0ykDVHhm1X/sSlyDblFnPz/Fexra9m2fJjBl5wwC62GXzk+PVGyfl4Q2b35arBp2V8Qf/PSNNlRk+7uzs36rUNXcXWXO3HPvuzdvz+bFfy59WNOW7+0Kq+o3Or6/7bg7r1AInqa6Zm1mvlXbHqdh5QPoZ2KUstMmIvYeWOOWmIa/NnRcT1balujw/faYHCrHL5tefl3N9zWPZ/FuPqgw9a1zJ87I2y91BYzPh0NYlrLm7Pg9OvD4LrFFTRopZO/P3+egxY3PZCxub8KBSSHW/0/LLuybm3EEtc4Zq/WvzsrCutEe2yr79s4uBXcpCZQYM3z3tS3xJrW7h/Lxm1HEb3s/n59nn3rbEm9hls3Nj1nWZNH3T5oFSNSJjxu3z978sU9E/oycclXZNr93UPj4xkyxAXh42Lswffzo+ow45P1fP2dCE0K3ITvt9Jtc9fH0+sUeblntevbYwr5d0CBdS1XPndHclpExU7bpb+pX4cNbw+oISzwtKsn5mnpljXFfs8jfqM2Py5Dxft/mNt3rkuIzbY2uMuhWy8+kTcnwJX1UX62bkmppnfRXTjG1a+kxu/PEFOXrYiJz41esya20TxnOreuSQCydl2tRLc/quLXkx2WLefuPNrCuWdp506dUjltilXFTusmv6lhi7jauXZOkG226b3dFnT8/zG0wCbNYPiTbBe6DuqUy+bvYWfh64OgeMG5OhW+kr1UK3UzLhpK65dfLyJn69Up85k67OY9/aP0eYw9gMNGT9soWZ89xTefzRR/LQvXfk7sfmZ1V9U9cWa50+h52X7/zsuzlvVPe0/G/qG/PW0rdK/CqxIp27dvbUT9kodOyVXu0KSSlv/je+lSXLGpOdHOnb4iF7xXPPlvbz5IjdHUHtozW5fv4Wfh649cEZN3rQ1ouSQuecOOHU9LjuyixpYgE0LLg+Ex/8QY44oZ0d9S5i67Vbv5NPvNY1Jb/6VWxI3aZNqa1dnzUr3sqyZUvy5quvZtGq2jSWOGBQqOqa4Sd8KJ/+whdz3nED0nYH2v7Lly0vOXZ36ryT2KV8VHRL964VycoS6qpxdVatbowvc7eFurww/flsMrArdvnv1uehmil5dbPrVCFtDhufM3fduhejDsdOyBl9JuY3TZ2w1bgoN028Jz894YzSf71N7GbF9Jvy++nb+/+3kEKb7hl28PE55fQzMnr0yRm1S9sUdsDtv2b1mhJjt5D2HdrHj6dRPrHbIZ06ViSlrCZdXJvVa9XYtrnsvJFnZyzzclpzP21sgu1s7Z8y6ZZFm58YhXZ53/jTs8vW3iNtj8iEs3YtYbS4Mctvvyq3WXO3TBRS1Xn3HD/uwxn7gcOz924906ahdgedd92QdWs2lLz92rRrI3Ypo1O+Xdq3K/GILW7I+h16Tml9Znxnn1T//18325qfyoH50iN+j7m5M7K7nb19T01uW7b5M2ChwzE5+7Sdt8FNt3UOOmdshvzyR5ndxAIqrr43E6e8mXM+3tfTULNXTP3bs/PH/5ydP/7XwVSR1l0GZt/D35ejjz42x514Qo4Y1rXlv4RV3Jj1GxtLXiu6dWsT1Cmv23Z1qXfuYn3q6w1gsOPSMtv1Zrw8d9bcmc3X/i9kp/efnZN7bJvxper9zsnZe5SQOsX1eciau2V8nDWmdsXLefzW3+WHXzwnx47onT57nZQLvl+TJxbVtuA/vD71pQ5pFypSWekySDmpTGXluzg3vECF2GW7NMjSW1Pzx9WbjzxVdMmJ409Kt231XWrliIw/Z7+0KmHN3U1PTMykWa6OLePA25S3Zt6dK741IYfsNiCjJvwgN89e3QJ/Le9dxG4q3kU4wHupMY0lD0RUpsLdHrHL9rhAvXFTTR7YwiKgFd1OzvgTOm/TkYDBY8/JIa1LWXP3uVxzzTOps+NKUJV9vjMjdcViiu/q05iG+rps2rAuq1cuy5uvzsvsGU9m6v135IarLsu/fe9r+exHzsixBwxO9zYV72rKS7F2cZ6s+WbO3HtoDvn4ZZm2vCUN31ekUPIVrfguwgHeSw1pKHUcolCZ6ioz09mR785sp9ZdmBsnTc3m7whUpOdpZ+e4jts4A/qPzjlHfj1//uO6Jo7o1Wfu5Il57NsH5EhTGreTQioqq1JRWZXqNu3SsXP39O43MLtv6T9atzLznp6aB+6/J3feekv++NQbWVfCGmXFuiV54vJP5fDba/KF/7gy3zt9t5T/bn43cxkb0tBgLiNlpFibDRtLnpme1q125I1WkW4HjM7o/btu/fdiiivy9I035qnlnprFLml4+fpMerx289Cs7JPTxx+V9tv8XO+TMyYcm6/ce2tWN/E62bDwhkx84F9y5Int7cDmprpLBh18agYdfGrO/8YvsvrlBzL5t5fm57+9PbNWNTTxgaaYukV/zr+edVAe/+rETP7eCelTzl/pF1qndavSb2W1tbVJ2jimKJPY3ZD1pcZuRYd07LAjf5FbkV1O+WZ+9e29t370FFdm4uI78pFb18djc3M+AtgeqZtZ107K9C2sOl3Z78yMP2J7LPtfSPdTJ+TELiXs8sbFuXniPXnbDmz2p3Gnwcfm4z+5Oc/Nn5Y/XHhEdq7u2SMQAAAa20lEQVQuYcpKw7I8/KMP5shzr87LZb2CTqt07NiqxJGbYjas2+AmRRnF7tqsKXXN3EKHdOxgGsO2ubV2zF4jB8XUf7FL/bOZPHnmFua/Vma30eNyyHb6/rjQ5aRMOKV7CTv9L2vuLpMC5aKy6375yM8fyIz7/yUn7VLd9PAr1mbepI/lxAtuyOtl+15iRTru1KnEi1pj1qxZa0F4ykfjqqx8u8QjtqJrenR1u982qjJ0373jWULs7vA2PTk5183ZwmviVUNy1rhR2X5TqTrmuAmnp3cJe7245t5MnPKGGCiv5E3PI76WWx6+Jh/dvYQfTChuyryJ52Xsj6dnQ1n+3RXp3rN7iRe1Yt5evtLILuVj/fKsKPEHIgqtuqdHZzW2rbTZa98Mr7YdmvcjCdtYbR6ddH3mb2G0rFCdPHf5Z/PJ323Hf5y6+WldWUia+jJTcUP+PPG6vHLBlzLIo1FZqR4wJpfdvjrLD/94bl3SxMeV4po8/t2P5uKjHsuPDi23udoV6blzz5JHdle8tcLDHGWjYeniLC11YLdX3/T2Pfu2u/L02yf7dKvIY4tcScTuDvsU/mBqpry2xV8xL26YnTsvn93M/4BiaqddnZoXP59v7elqWXbBO/i8XPGbP2f/s67Ka02cnlCsnZmfX/hvOfvRb2WfsnqDu5DO/XZJx4qktsn3nMasWrQoa4tJVwNflIHGxW9kcYlNVdlvt/Rz+d6GF9o9s++e1SksqvUtUXN9ILEJtq0199fklnJ/2qt7PjXXTLfmblkqpMdpP8z3T+lawslezMZnLsnF1y8puwt31YBB2bXEm3rjm6/nTQMylIl1ry4ocWS3Ih0HDEh3d/tteJntlr1H9hdUYncHVXw799TcnrfK/kZan5cmT8yjG+3S8jzL+2TcP52foaV8j9O4Mnf84srMKbOX1Sr7D83gNqUN0TYsnJeF9Q4TyuNavHDewtSX9BRalaEjhsaU0m2pOiP22yttfTskdnfI1l1+R2ruWtki5gM2vHpDJj6wzk4tU60P+GjOHVnC6gwpZtP0mkx+ocwqsM2I7D20tNlZjWvn5WVz7SgLtXlp1isp6Rm0olOG7dHPzX4ba7/XvhliYqjY3QFTN0turcm9q1vIDJ7GJbn5qrvytglJ5alyaE45dY/SJunXz8pdd81LWQ3uVg7MPnvtVNqFrX5Onp9tkg5loP7lPD+rxHWhq/bOAXsb193ml54hJ+aC8z6aj3zkI+98Pnx8hrQx1Ct2W7rGN3LTpAezbgtXpep9v5vn64spFt/DT/3z+e7IUi6AjVlx58Tcas3dcr0UZ/cjDkuvkuaz1mfm1MfL7AGndfY/dL+U9ENqjSsz87mFaXCQ0MwVV8/IMy+XdqRWDTggB/Ryq9/mWh2QT/3mD7nyyivf+Vz+5byvk80idlt66y68IZMe3sITeKE6I8eOyfD3+s3YyuEZPWbvkuZxFdfcl4k3vm6ZpjJVPWKfjChpaLeY2hefy5yymslQSI+DD8uw0oaw8+K0Z2KSDs1d3bNPZHptKU+fFely6BHZ09friF22voa8dN2kPLGFnwcuVB+QsaOHNIOfFqzM7qPHZN8SflY2xQ15+OrrtrhmMGWQgV12za6dS1yFdtGCvFpbXn9n5bBjc0xJ6ywVs37a1Ezf5Bihed9X5kx9tLSVGAptc8gxh6atjYfYZetfk17MtZOfyeatW0irg8Zm9KDmseBh5eAzM+aA0l5aqn3y6tS8qHbL82zvVvJPhhYbVmb522U2ll+9f048rldJF7eGRQ/nodmWZKAZa3w9D/5pVko5SgttDslJx3SOmaOIXba6+mcn59qZdVu68uTQcWdm1+ay1SsH5cwxo0qb31g3MzXXPG3N3XJUaJO2Ja+NU5va2nL7Q9vksDNPzs4l1e6s/PHehabo0GwVl92Xu57cVMLLaYW0GnVaTurtNg/Ogq1uU6ZNum6L8xwLbY/I+NN3aUYbvSIDzhiTg1uXEkD1eXnyxDxizd0yvFs2pL6+1LfNKlNZhr+81O7IMTm1lJt8sS7T77grr6tdmufJm6V33pSHN5Rw/hba5LAxH0w/d3kQu1vdxkcy6YYtrYNYSPujxuW0Ps1rk1f0PyNjDm1T0tdcDa/dkKvuX2tfl50N2VDqQ0qhbdq1K8MvQdu+Lx85e1AJS60VU/vYDbn51RZWu42Lc9cPPpHzzz//nc8Fn8vvZpiuUX778c3ceu2WV/f5H0/d9kdnwmjr64LY3QbWPzQpU17bwpzWQsccM/7U9Gpu3VDRN6ePPby0X35pXJpbJ96VlVYhK7Mb5uq8vbq0mCtUd0nXjuU4469VRv3DP2TfEuboFGsfyzXXzm1ZS5DVTc/1l1yR3/3ud3/51GSq4euy0zB/cq58cH0JUxgq0uP0C3JmL7N1y0cxi688K/1775ydd37n0++UX9ssYrc5WpP7am7J4i3cSwqdjs/4k7s3wxcFKtLntLE5oqTRO2vuluWldM3reX1laaFTsfMu6VOmyxZVDj8vF36gc9MvcsVNmX7lH/JUC1qVoeGV5zJzVeN/f3pJtd8XKDO1mXbZbzOtlCXHqgbn3E+flM5at4xsyBP3P5A3Fi/JkiXvfJZtqrRZxG4zjIm3707N7W9t4SWXinQ5cVxO6tI8rzyFnU/NuKPalxTixbX3ZeIN1twtq/BZOC8LSxy2rNptaAaW6xqdhV4ZfdF5GVzV9CO7fu6VufT2lWkpj3EbX5jxN+8PVKXamqvldV9ZemN+9oeXS1iFoZCOx3wpnzuotY1XTuqfz6PTVv/VPbXQ0a9SiN3md0nK8ttrcveWRs4quuakcSc036fsQs+cMvaYdCipdjdm6tXXZp5VyMrm+Fz57PQS91dldtl7r3Qt49Ghtod+Od88pUvTL3SNy3LjTy9Py1iFrD4vzZiZ9f+93AtVqa423FdGjyt58pIf5tYVTR9WKFQNzse+8eH0d3cvK42vP57HFtT/1UNL244dbRix29xaYkluqbk3q7cwJFTR45SMP745P6EV0v3ksTmupLmZf1lz9wUvu5SHDXly6tNbWPv5fzssOmXUYXulrL/1LvTO+O9elAObPE2nmI3T/j3fu/mtFjC6uzYzn5v/N3OQq43slpGGuZflq5e+kLomH4wV6Xnm9/K1I9rZeOV2tk57JDPq/npfduzUwYYRu83tqWxKJm3xBYKK9Dp1XI5p5sdsoetJGfv+nUqbU1z/Qmqufip+eKocWveR3PrHt0qadlJod1hOOLx92f/p1Xt9IZd8bo+mryfduCTXf+P7+XO5LzhSNyszXqj962tSoSpVVUZ2y6N05+fyCy/Ow2tLeC2t87H59r+cFe+lld3JmmcfmfY3q20U0qGTkV2x27xSNwtumJSpG7dwUarskw+Oe1+a/XN2oUtOHHtCupR0RNRn3rVXZ6o1d5u91ffV5NZFJaVu2h91Zk7s3hLumm1z8Dd/nQtHtGriw1wx9S/9Jhf+y+NZX8Z/dXHFzDz32t/ucy+olYdNefHS8/NP965s+gNqoVPed/Evc8EgLzWV44PNY4+/+TffwhSM7Ird5nagzs11k57Y4lfElbuckfFHlMMvkxfS+YSxObG02k3D6zdk4n3W3G3ez2Kv5ppfXL/FVUL+5ytDt5zykQ+mZ0sZIepweL5zxVcysqlr7BVr8/y/XZBv/3l12U5nqHvx2bzwt99/F6pSbWS3uT+mZMUD/5Szv/5gmv5L3RXpftK/5vJPD41ZKmW4x99+Io/MrN9snxrZFbvNq3VfuDaTn6nbwk2xMgNGj8sh5fJSbKfjM/YD3Uo7KBqX5dar7swKq5A13xvnnd/JDx9YV1K0VQ3+SD53ate0pCxqf/A/p+bHx6ZrEw/wYu0LueTcz+amReW45khjFj33fJZt9o9eZWS3mVv37CUZP+6SPNfkX0srpHq3D+WyKz6WQUq3LG2a/kie2uybYSO7YrdZqc8zk6/NFt/TqhqU0WNHpVXZ/C0dc+zYk9OjtNrNyruvyi1L1W6zTN3l9+TrX5qY10tZhaGiW07+xpdycJuWtjVaZdhnr85/nr97mvYL2cXUL7wm5439YZ4quy8v6vLCjFmbL1dVqDay24wfTFc/9bOMPukruW9Z0x+wKnY6LBff8Ouc2dvtvFwbYs4jj2/+YFponY4dW9k8Yre5PJI9kUnXzd3iGohVQ8/K2P3LaxilwzHjckqv0g6L4to/ZeINr1lzt9kdmy/l8o9+NJe/VF/CqG4hnY74Zn50Tp+WeXEo9Mopv7g5Pz+5dyqb1HyNWfXIt3PquF9l5oYy+jsbXslzM1dt4Zy0zm4zPVnzyk1fyLHHfTX3LG5o8vlaaLd3PnfdlHxtP6svlO8zzlt5/LEt/HJjRcd06iDRxG4zsXHqpNywYEvDZlUZMWZs9im3G0u792Xsqb1LOzCKG/PIxMl52Zq7zejAnJ3fTzg+n71jSUkPIRVdjsn3f/OZDGvJQdR6WD4x+c789PieTQveYkMW3/X5HPfBn2TaqjL5BmP9zDw7ZwuP4IVq6+w2N+vm5LovHJ2Dxlyap1aVsJ5u+73zqcl35mfv7xF7tIxteDKPPL1p8wecQsd06mjPit3mcZXKg5OmbPkr4uq9M2bMnmX4skDbHDnug+lb0gu9xWx6+prUzLTmbrPo3PlT8sWjDssFNy4sYX3OpFC9Wyb89j/zmWE7wKTO9iPzxSn35dLT+qW6icG75N6v5bgjP5Gauc17iHf9S1Ny0Qc+nRvWFLf4EG5kt5kors2cm7+d00bun/E/fzTLGpo+R7ei22H5+q335dJT+8baC+Wt/oVH8sSWHnIKHdKpg9gVu83B6vtSc8uWRs4Kqd53bMYMK8/LUJvDxuT0XSpLPWNTc/WT1tx9Tytnfu78lzHZb+SYXPLEijSWErqVO+fEf7sll53Vd8e5KLTfK5+67qHUfGKfdGziHN41z12eD486NBf8fkaa2yBvce2c3Pyd0zNy37Pys6nLsuV28oLae183b+XZG3+Yjxw8OHud+d3c9vK6kqYZddjjI7ly6r35/jE93MDLXmPefOyxvLKlcSIju2K3mdxasvLumty+fEtPZNU5cOzoDCnXR+7Wh2bMGbuWOGLQkPnXTszDGxwZ21dDVr38YK781vjsv9vwnPKNGzJrTWmzpwutds0Zv7o3N3x2r7Td0TZfq91y1n/8OQ/9amyGNHFZssZVz+aK8w/KnsdflKufXfGez1WvWzY9133/wxk1aK+cefEteWnd/5JOXlB7j5pmTRY+dmN+8ZWxOXhA/+x31tdz1bQlJX3zkopO2esfrsgjj/0+Hx7W1jZtEdZl2qMzUrfF/d3BnN2tyBda77p138rtNXdvcR3EQuuDM270wDJ+kmiVg8eckd0u/VlJ83Ab3piSiff9OMeeam3AbXfT3JgVr72UF555Io9MnZo/339PHpixOBvf1ShjIZU9j8g/XlWTi0/ou+NeDAqdsu+nJufJg47OZ8/9aq55Yc3/PdJWrM3r9/8s5x7wh/zrmZ/ORRd9JmNG7Zzttspg7ZI8e++NmXz1Vbn61ml5o8nLVFWlylV/26pfl6WvvpyX5szK889Oz1PTHs3Uh5/K3OW173LN5kLa7HpCvnLpr/L1UwemjS1cvkMTG1dl+bKlWbLojby2cEHmzXk0k+/f8vWm0KpjOtrZYvc9b93Ft6Tmvi0dpIW0OXRczuhf3k9k1QeOyZmDfp6fzC1hHm7jstw68c6sOGVcuraAwaPiplVZsaaUeciNef327+czi7bO+rTFhk2pra1N7Ya1WblsaZYsWZTXX309y9c3/P0/dFBom4GnfCOX/fqrOa6v77WTQnba75OZ+OTxGfODz+TCn/0xC5vwBFFsWJEZ138/H77hJ/nyyJNzzoRxOevMk3Lwbp227lzKxvVZ9MITmTr14Tx03z256/5peWVVfcnHQaGyOtUtbrCoPrWbGks6T+df/amc/FjHrXCeFtNQV5dNteuyZtWqrHxrSRYvW5WNDcWtckxWdByaU7/0w/z4otOzu/mbf88JtFWvzSkW01hsTGNDQxoaGtLQUJ/6urps2rTpnU/txmzYsCEb1q/PunVrs2b1qqxatTprN9Y1eXpZoUOnmMWwFa/wxWLRAqn/19PY2kV56YXn8txzz+f55///Z1YWrNi4hQO3kIo2XdJv8O4ZOnTo33wGZ9ce7ZrXCwXFuqxe9Epemjs3c//rM+edf52/KKvrSjs8ChVt023XoRk2fFiGDxuWYcOGZ9jwYRk2bGgGdG/bDEe7G7Nx+at5ac7szJ49J7Nnz86c2bPf+ddXlmRtfQs7PQoV6bT7B/OlH/4oF50+NO2d3lu0bu7N+ek/fi3/duvcrGks8RgotErXwQfk8MMOzqgD988+ew7PsCED06/XTmld8b+HU/26FVmyeHEWv7EwL82elVmzXsyLLz6fZ56ckfkrN/3dDzmFzufmlsX/mVNbl98+KW56O6+/vPl5OnvO/Ly5ZlNazp2skMrOw3PKJ/4xX//S2RnVc8d+GK1fuzivzP3bfT47s+e+mhUbG9NSA6Zq93/MIzN/lFGGJMXutjLvvt/lpodm/CVqZ2b2guXZ0LgVNlOhIq136ptB/y2Adz/gxIw9cVi2y0N749I8deNNeWDmnMyd85ewfXlBlqyp2/YXjEJl2nbtnyHDhv0lhEfkwBPH5gN7dNruy+Y0zJ6cb/zs5jw3e3bmzHkpC99an4aWfhYUWqfX/qfn41/8ai4cu1+6u4A25TabpU9ek599/8f57R2z8/bfdZAUUqhuly7duqbLTp3Srk2rVFcmDXW12bhxY2o3bszat5dnxdraNG7DY7Gi+3m5443f5cQyWKu+Yd5t+elv7njnPJ09O3MXLG15D59/dYi0yc77npQJ538yn/zQ8Rm8Aw7rNS56LJOnPJSZ/z9s58zJvDdWpbZxx8uU6v1/kBlPfD3DLbexlZ6W2cyvjmlVTLJdPtX7/6D4Yv12+sNq7yle0Ktiu/1t//unqrjPd2YU696D/bvxpnOKHQtpJtthG34KFcX2/UYVz7jwX4s3PLmouNGp/S41FFfNvrP488+fVty7R6tioayOg0Kxol3v4j7Hf6j45R9fWbzrmTeLG8pkq+8I52mhol2x7/6nFD9x8RXFu19Y/p5cD+3z5vlpddSlxVcbXH23FuM70GJGhirTtsegjBx1aA4/8qgcc9zxOXJkn7Qz7+vvHQ9Np91PyoWXnJQLf7Ikz959Y667cUpuvuPhzF6+qVl9jVootMpO/UdkvwMPyiGHHvrOZ9/B6epXR5vD3klF2+4ZvM8BGTXq4Bxy+JE56n0HZ3jPNn4Ugs2vOh06paPFGLYasQvN+v5YSEWhMpXVrdK6bdu0b98xnTp3TtduPdNr597p069/BgwYmMFDh2X4HntkaN+OTuptqVWvjDzt0xl52qfzLw1rsvDph3Lfnx7K1Ecfy+PTnslLS7fPlJhCRat03Hm3DBk6NLsPHZrdh++RvfYZmZEj98iALq3E03Y7PStSUVmZyqrqtG7TLh06dkzHTl3StUeP9OzZMzv36ZddB+yW3QYOzu7Dhmf3gb3S3tfSNOHBqKpjx1hgbituUXN2AbaGhqxbMi8vznwxs1+al/nzX8nC197IosVLsnTZ8qxcvTpr1qzNuo2bUl9Xn/rGpFBZmarKqlRVVaW6VZu0bd/+nWDq0DGdOndNt+7d06NH93Tv0TO9evdNv379079///Tv1yfd23usARC7AADs0MwIAQBA7AIAgNgFAACxCwAAYhcAAMQuAACIXQAAxC4AAIhdAAAQuwAAIHYBAEDsAgCA2AUAQOwCAIDYBQAAsQsAAGIXAADELgAAiF0AAMQuAACIXQAAELsAACB2AQBA7AIAgNgFAACxCwCA2AUAALELAABiFwAAxC4AAIhdAAAQuwAAiF0AABC7AAAgdgEAQOwCAIDYBQAAsQsAgNgFAACxCwAAYhcAAMQuAACIXQAAELsAACB2AQAQuwAAIHYBAEDsAgCA2AUAALELAABiFwAAsQsAAGIXAADELgAAiF0AABC7AAAgdgEAELsAACB2AQBA7AIAgNgFAACxCwAAYhcAALELAABiFwAAxC4AAIhdAAAQuwAAIHYBAEDsAgAgdgEAQOwCAIDYBQAAsQsAAGIXAADELgAAYhcAAMQuAACIXQAAELsAACB2AQBA7AIAIHYBAEDsAgCA2AUAALELAABiFwAAxC4AAIhdAADELgAAiF0AABC7AAAgdgEAQOwCAIDYBQBA7AIAgNgFAACxCwAAYhcAAMQuAACIXQAAxC4AAIhdAAAQuwAAIHYBAEDsAgCA2AUAQOzaBAAAiF0AABC7AAAgdgEAQOwCAIDYBQAAsQsAgNgFAACxCwAAYhcAAMQuAACIXQAAELsAAIhdAAAQuwAAIHYBAEDsAgCA2AUAALELAIDYBQAAsQsAAGIXAADELgAAiF0AABC7AAAgdgEAELsAACB2AQBA7AIAgNgFAACxCwAAYhcAALELAABiFwAAxC4AAIhdAAAQuwAAIHYBABC7AAAgdgEAQOwCAIDYBQAAsQsAAGIXAADELgAAYhcAAMQuAACIXQAAELsAACB2AQBA7AIAIHYBAEDsAgCA2AUAALELAABiFwAAxC4AAGIXAADELgAAiF0AABC7AAAgdgEAQOwCACB2AQBA7AIAgNgFAACxCwAAYhcAAMQuAACIXQAAxC4AAIhdAAAQuwAAIHYBAEDsAgCA2AUAQOwCAIDYBQAAsQsAAGIXAADELgAAiF0AAMQuAACIXQAAELsAACB2AQBA7AIAgNgFAACxCwCA2AUAALELAABiFwAAxC4AAIhdAAAQuwAAiF0AABC7AAAgdgEAQOwCAIDYBQAAsQsAgNgFAACxCwAAYhcAAMQuAACIXQAAELsAAIhdAAAQuwAAIHYBAEDsAgCA2AUAALELAABiFwAAsQsAAGIXAADELgAAiF0AABC7AAAgdgEAELsAACB2AQBA7AIAgNgFAACxCwAAYhcAALELAABiFwAAxC4AAIhdAAAQuwAAIHYBAEDsAgAgdgEAQOwCAIDYBQAAsQsAAGIXAADELgAAYhcAAMQuAACIXQAAELsAACB2AQBA7AIAIHYBAEDsAgCA2AUAALELAABb3/8D8NoKw+bgUIQAAAAASUVORK5CYII="
}

It should give output as

ABCDE

---

Refrences

https://stackoverflow.com/questions/33588262/tesseract-ocr-on-aws-lambda-via-virtualenv

https://gist.github.com/barbolo/e59aa45ec8e425a26ec4da1086acfbc7

This is not working :

wget https://github.com/tesseract-ocr/tessdata_fast/raw/master/osd.traineddata
wget https://github.com/tesseract-ocr/tessdata_fast/raw/master/eng.traineddata

fails .....

This are the correct links:
wget https://raw.githubusercontent.com/tesseract-ocr/tessdata_fast/main/osd.traineddata
wget https://raw.githubusercontent.com/tesseract-ocr/tessdata_fast/main/eng.traineddata