How to use AWS Textract to extract text from documents

Amazon Textract is an AWS tool that can be used to extract textual data from a document. It supports various types of documents, such as images and PDFs. It can also be used for document analysis.

Extract text from a document

Let's use Amazon Textract and get the text from an image. We will extract text from the image given below. This image has been stored locally and will be used later in a code.

Note: You can view the image by clicking the "Download" button on the top right side of the widget.

iVBORw0KGgoAAAANSUhEUgAABeoAAAE8CAYAAAC/04GKAAAKrWlDQ1BJQ0MgUHJvZmlsZQAASImVlwdUU9kWhs+96SGhJYQiJfQmSCeAlBBaKIJ0EJWQBAglxEBoYkPEERhRRETAho6KKDgqRQZREcU2KCp2HZBBRBkHCzZU3gUWYWbeeu+tt9fa63zZ2ec/+5x1z137AkCW54hEKbA8AKnCDHGwtzs9MiqajhsGEIABEcgDPQ43XcQMCvIHiM2Of7cPd5FsxG6bTWn9+///1RR4/HQuAFAQwnG8dG4qwqcQf8kViTMAQO1F4rpZGaIp7kKYKkYKRPj+FCfM8OgUx00zGkznhAazEKYCgCdxOOIEAEh0JE7P5CYgOiQ3hC2EPIEQYRHCLqmpaTyEjyNshOQgMdKUPiPuLzoJf9OMk2pyOAlSntnLtOE9BOmiFE7O/3kc/9tSUySzaxggTkoU+wQjoyJyZveT0/ykLIxbFDjLAt50/jQnSnzCZpmbzoqeZR7Hw086N2WR/yzHC7zYUp0Mdugs89M9Q2ZZnBYsXStezGLOMkc8t64kOUwaT+Szpfq5iaERs5wpCF80y+nJIX5zOSxpXCwJltbPF3q7z63rJd17avpf9itgS+dmJIb6SPfOmaufL2TOaaZHSmvj8T0853LCpPmiDHfpWqKUIGk+P8VbGk/PDJHOzUAeyLm5QdIzTOL4Bs0yYIE0kIK4GNCBP/LLA4AMfnbG1EZYaaIcsSAhMYPORG4Yn84Wcs3n060srKwBmLqvM4/DO9r0PYRoV+di+e8BcOZNTk62z8X89QA4tQEA4vO5mGEHALLKAFwu5krEmTOx6buEQd4CcoAKVIEm0AVGwAxYATvgBNyAJ/AFgSAURIFlgAsSQSpSeRbIA+tAISgGW8B2UAX2gP3gMDgGToAW0A7Og0vgGrgJ+sAj0A+GwCswBj6ACQiCcBAZokCqkBakD5lCVhADcoE8IX8oGIqCYqEESAhJoDxoPVQMlUFV0D6oDvoZOg2dh65AvdADaAAagd5CX2AUTIKpsAZsAC+AGTAT9oND4aVwArwCzoUL4M1wJVwLH4Wb4fPwNbgP7odfweMogJJB0VDaKDMUA8VCBaKiUfEoMWo1qghVgapFNaDaUN2o26h+1CjqMxqLpqDpaDO0E9oHHYbmolegV6NL0FXow+hmdBf6NnoAPYb+jiFj1DGmGEcMGxOJScBkYQoxFZiDmCbMRUwfZgjzAYvF0rCGWHusDzYKm4RdiS3B7sI2Ys9he7GD2HEcDqeKM8U54wJxHFwGrhC3E3cUdxZ3CzeE+4SXwWvhrfBe+Gi8EJ+Pr8AfwXfgb+GH8RMEeYI+wZEQSOARcgilhAOENsINwhBhgqhANCQ6E0OJScR1xEpiA/Ei8THxnYyMjI6Mg8xiGYHMWplKmeMyl2UGZD6TFEkmJBYphiQhbSYdIp0jPSC9I5PJBmQ3cjQ5g7yZXEe+QH5K/iRLkTWXZcvyZNfIVss2y96SfS1HkNOXY8otk8uVq5A7KXdDblSeIG8gz5LnyK+Wr5Y/LX9PflyBomCpEKiQqlCicEThisILRZyigaKnIk+xQHG/4gXFQQqKokthUbiU9ZQDlIuUISqWakhlU5OoxdRj1B7qmJKiko1SuFK2UrXSGaV+GopmQGPTUmiltBO0u7QvyhrKTGW+8iblBuVbyh9V5qm4qfBVilQaVfpUvqjSVT1Vk1W3qraoPlFDq5moLVbLUtutdlFtdB51ntM87ryieSfmPVSH1U3Ug9VXqu9Xv64+rqGp4a0h0tipcUFjVJOm6aaZpFmu2aE5okXRctESaJVrndV6SVeiM+kp9Ep6F31MW13bR1uivU+7R3tCx1AnTCdfp1HniS5Rl6Ebr1uu26k7pqelF6CXp1ev91CfoM/QT9Tfod+t/9HA0CDCYKNBi8ELQxVDtmGuYb3hYyOykavRCqNaozvGWGOGcbLxLuObJrCJrUmiSbXJDVPY1M5UYLrLtHc+Zr7DfOH82vn3zEhmTLNMs3qzAXOaub95vnmL+esFeguiF2xd0L3gu4WtRYrFAYtHloqWvpb5lm2Wb61MrLhW1VZ3rMnWXtZrrFut39iY2vBtdtvct6XYBthutO20/WZnbye2a7Absdezj7Wvsb/HoDKCGCWMyw4YB3eHNQ7tDp8d7RwzHE84/ulk5pTsdMTpxULDhfyFBxYOOus4c5z3Ofe70F1iXfa69Ltqu3Jca12fuem68dwOug0zjZlJzKPM1+4W7mL3JvePLEfWKtY5D5SHt0eRR4+nomeYZ5XnUy8drwSveq8xb1vvld7nfDA+fj5bfe6xNdhcdh17zNfed5Vvlx/JL8Svyu+Zv4m/2L8tAA7wDdgW8HiR/iLhopZAEMgO3Bb4JMgwaEXQL4uxi4MWVy9+HmwZnBfcHUIJWR5yJORDqHtoaeijMKMwSVhnuFx4THhd+McIj4iyiP7IBZGrIq9FqUUJolqjcdHh0Qejx5d4Ltm+ZCjGNqYw5u5Sw6XZS68sU1uWsuzMcrnlnOUnYzGxEbFHYr9yAjm1nPE4dlxN3BiXxd3BfcVz45XzRvjO/DL+cLxzfFn8iwTnhG0JI4muiRWJowKWoErwJsknaU/Sx+TA5EPJkykRKY2p+NTY1NNCRWGysCtNMy07rVdkKioU9a9wXLF9xZjYT3wwHUpfmt6aQUUao+sSI8kGyUCmS2Z15qes8KyT2QrZwuzrOSY5m3KGc71yf1qJXsld2Zmnnbcub2AVc9W+1dDquNWda3TXFKwZWuu99vA64rrkdb/mW+SX5b9fH7G+rUCjYG3B4AbvDfWFsoXiwnsbnTbu+QH9g+CHnk3Wm3Zu+l7EK7pabFFcUfy1hFty9UfLHyt/nNwcv7mn1K509xbsFuGWu1tdtx4uUyjLLRvcFrCtuZxeXlT+fvvy7VcqbCr27CDukOzor/SvbN2pt3PLzq9ViVV91e7VjTXqNZtqPu7i7bq12213wx6NPcV7vuwV7L2/z3tfc61BbcV+7P7M/c8PhB/o/onxU91BtYPFB78dEh7qPxx8uKvOvq7uiPqR0nq4XlI/cjTm6M1jHsdaG8wa9jXSGouPg+OS4y9/jv357gm/E50nGScbTumfqmmiNBU1Q805zWMtiS39rVGtvad9T3e2ObU1/WL+y6F27fbqM0pnSjuIHQUdk2dzz46fE50bPZ9wfrBzeeejC5EX7nQt7uq56Hfx8iWvSxe6md1nLztfbr/ieOX0VcbVlmt215qv215v+tX216Yeu57mG/Y3Wm863GzrXdjbccv11vnbHrcv3WHfuda3qK/3btjd+/di7vXf591/8SDlwZuHmQ8nHq19jHlc9ET+ScVT9ae1vxn/1thv139mwGPg+rOQZ48GuYOvfk///etQwXPy84phreG6F1Yv2ke8Rm6+XPJy6JXo1cRo4R8Kf9S8Nnp96k+3P6+PRY4NvRG/mXxb8k713aH3Nu87x4PGn35I/TDxseiT6qfDnxmfu79EfBmeyPqK+1r5zfhb23e/748nUycnRRwxZ7oVQCEOx8cD8PYQAOQoACg3kf5hyUw/PW3QzDfANIH/xDM997TZAdCADFNtEescAMcRN1iLaLsBMNUShboB2Npa6rO973SfPmVY5Itlr8sU9aksJYF/2EwP/5e6/zmCKVUb8M/xX0NeBcsKcbxsAAAAimVYSWZNTQAqAAAACAAEARoABQAAAAEAAAA+ARsABQAAAAEAAABGASgAAwAAAAEAAgAAh2kABAAAAAEAAABOAAAAAAAAAJAAAAABAAAAkAAAAAEAA5KGAAcAAAASAAAAeKACAAQAAAABAAAF6qADAAQAAAABAAABPAAAAABBU0NJSQAAAFNjcmVlbnNob3QVPGZCAAAACXBIWXMAABYlAAAWJQFJUiTwAAAB12lUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNi4wLjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj4zMTY8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpQaXhlbFhEaW1lbnNpb24+MTUxNDwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgotCPgkAAAAHGlET1QAAAACAAAAAAAAAJ4AAAAoAAAAngAAAJ4AADFfhhxWHAAAMStJREFUeAHs3Qe0PFV9B/DBmogNBEVRwIKIIKhBA2Jiwd4SMEYRCxo0iqKYKMZKFGKIwLGCYEOiUoyRWLCBBaUcjQgRkSBgVyIapIiFqJP9bZzJnbuz7e3um328z5zz/+/szsy9dz5z3+453529d4OytxQWAgQIECBAgAABAgQIECBAgAABAgQIECBAoBOBDQT1nbirlAABAgQIECBAgAABAgQIECBAgAABAgQI9AUE9ToCAQIECBAgQIAAAQIECBAgQIAAAQIECBDoUEBQ3yG+qgkQIECAAAECBAgQIECAAAECBAgQIECAgKBeHyBAgAABAgQIECBAgAABAgQIECBAgAABAh0KCOo7xFc1AQIECBAgQIAAAQIECBAgQIAAAQIECBAQ1OsDBAgQIECAAAECBAgQIECAAAECBAgQIECgQwFBfYf4qiZAgAABAgQIECBAgAABAgQIECBAgAABAoJ6fYAAAQIECBAgQIAAAQIECBAgQIAAAQIECHQoIKjvEF/VBAgQIECAAAECBAgQIECAAAECBAgQIEBAUK8PECBAgAABAgQIECBAgAABAgQIECBAgACBDgUE9R3iq5oAAQIECBAgQIAAAQIECBAgQIAAAQIECAjq9QECBAgQIECAAAECBAgQIECAAAECBAgQINChgKC+Q3xVEyBAgAABAgQIECBAgAABAgQIECBAgAABQb0+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOhQQ1HeIr2oCBAgQIECAAAECBAgQIECAAAECBAgQICCo1wcIECBAgAABAgQIECBAgAABAgQIECBAgECHAoL6DvFVTYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBPX6AAECBAgQIECAAAECBAgQIECAAAECBAgQ6FBAUN8hvqoJECBAgAABAgQIECBAgAABAgQIECBAgICgXh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIdCgjqO8RXNQECBAgQIECAAAECBAgQIECAAAECBAgQENTrAwQIECBAgAABAgQIECBAgAABAgQIECBAoEMBQX2H+KomQIAAAQIECBAgQIAAAQIECBAgQIAAAQKCen2AAAECBAgQIECAAAECBAgQIECAAAECBAh0KCCo7xBf1QQIECBAgAABAgQIECBAgAABAgQIECBAQFCvDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQ4FBPUd4quaAAECBAgQIECAAAECBAgQIECAAAECBAgI6vUBAgQIECBAgAABAgQIECBAgAABAgQIECDQoYCgvkN8VRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAUG9PkCAAAECBAgQIECAAAECBAgQIECAAAECBDoUENR3iK9qAgQIECBAgAABAgQIECBAgAABAgQIECAgqNcHCBAgQIAAAQIECBAgQIAAAQIECBAgQIBAhwKC+g7xVU2AAAECBAgQIECAAAECBAgQIECAAAECBAT1+gABAgQIECBAgAABAgQIECBAgAABAgQIEOhQQFDfIb6qCRAgQIAAAQIECBAgQIAAAQIECBAgQICAoF4fIECAAAECBAgQIECAAAECBAgQIECAAAECHQoI6jvEVzUBAgQIECBAgAABAgQIECBAgAABAgQIEBDU6wMECBAgQIAAAQIECBAgQIAAAQIECBAgQKBDAUF9h/iqJkCAAAECBAgQIECAAAECBAgQIECAAAECgnp9gAABAgQIECBAgAABAgQIECBAgAABAgQIdCggqO8QX9UECBAgQIAAAQIECBAgQIAAAQIECBAgQEBQrw8QIECAAAECBAgQIECAAAECBAgQIECAAIEOBQT1HeKrmgABAgQIECBAgAABAgQIECBAgAABAgQICOr1AQIECBAgQIAAAQIECBAgQIAAAQIECBAg0KGAoL5DfFUTIECAAAECBAgQIECAAAECBAgQIECAAAFBvT5AgAABAgQIECBAgAABAgQIECBAgAABAgQ6FBDUd4ivagIECBAgQIAAAQIECBAgQIAAAQIECBAgIKjXBwgQIECAAAECBAgQIECAAAECBAgQIECAQIcCgvoO8VVNgAABAgQIECBAgAABAgQIECBAgAABAgQE9foAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoUEBQ3yG+qgkQIECAAAECBAgQIECAAAECBAgQIECAgKBeHyBAgAABAgQIECBAgAABAgQIECBAgAABAh0KCOo7xFc1AQIECBAgQIAAAQIECBAgQIAAAQIECBAQ1OsDBAgQIECAAAECBAgQIECAAAECBAgQIECgQwFBfYf4qiZAgAABAgQIECBAgAABAgQIECBAgAABAoJ6fYAAAQIECBAgQIAAAQIECBAgQIAAAQIECHQoIKjvEF/VBAgQIECAAAECBAgQIECAAAECBAgQIEBAUK8PECBAgAABAgQIECBAgAABAgQIECBAgACBDgUE9R3iq5oAAQIECBAgQIAAAQIECBAgQIAAAQIECAjq9QECBAgQIECAAAECBAgQIECAAAECBAgQINChgKC+Q3xVEyBAgAABAgQIECBAgAABAgQIECBAgAABQb0+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOhQQ1HeIr2oCBAgQIECAAAECBAgQIECAAAECBAgQICCo1wcIECBAgAABAgQIECBAgAABAgQIECBAgECHAoL6DvFVTYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBPX6AAECBAgQIECAAAECBAgQIECAAAECBAgQ6FBAUN8hvqoJECBAgAABAgQIECBAgAABAgQIECBAgICgXh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIdCgjqO8RXNQECBAgQIECAAAECBAgQIECAAAECBAgQENTrAwQIECBAgAABAgQIECBAgAABAgQIECBAoEMBQX2H+KomQIAAAQIECBAgQIAAAQIECBAgQIAAAQKCen2AAAECBAgQIECAAAECBAgQIECAAAECBAh0KCCo7xBf1QQIECBAgAABAgQIECBAgAABAgQIECBAQFCvDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQ4FBPUd4quaAAECBAgQIECAAAECBAgQIECAAAECBAgI6vUBAgQIECBAgAABAgQIECBAgAABAgQIECDQoYCgvkN8VRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAUG9PkCAAAECBAgQIECAAAECBAgQIECAAAECBDoUENR3iK9qAgQIECBAgAABAgQIECBAgAABAgQIECAgqNcHCBAgQIAAAQIECBAgQIAAAQIECBAgQIBAhwKC+g7xVU2AAAECBAgQIECAAAECBAgQIECAAAECBAT1+gABAgQIECBAgAABAgQIECBAgAABAgQIEOhQQFDfIb6qCRAgQIAAAQIECBAgQIAAAQIECBAgQICAoF4fIECAAAECBAgQIECAAAECBAgQIECAAAECHQoI6jvEVzUBAgQIECBAgAABAgQIECBAgAABAgQIEBDU6wMECBAgQIAAAQIECBAgQIAAAQIECBAgQKBDAUF9h/iqJkCAAAECBAgQIECAAAECBAgQIECAAAECgnp9gAABAgQIECBAgAABAgQIECBAgAABAgQIdCggqO8QX9UECBAgQIAAAQIECBAgQIAAAQIECBAgQEBQrw8QIECAAAECBAgQIECAAAECBAgQIECAAIEOBQT1HeKrmgABAgQIECBAgAABAgQIECBAgAABAgQICOr1AQIECBAgQIAAAQIECBAgQIAAAQIECBAg0KGAoL5DfFUTIECAAAECBAgQIECAAAECBAgQIECAAAFBvT5AgAABAgQIECBAgAABAgQIECBAgAABAgQ6FBDUz4hflmXx9re/vfjZz35Wl7T//vsXf/AHf1A/n3Tlk5/8ZHHuuefWu//Zn/1Zse2229bPF7ly4oknFt/+9rfrKvbZZ59ik002qZ+P217v2NHKaaedVpx11ll17TvttFPxkIc8pH4+buWwww4rfvOb3zR222+//YoNN9yw8dqwJ6effnoR/6plq622Kp70pCdVT5ficRHX8Mwzzyy+8IUv1OeX95t6wxQrxx57bHHppZdOccTwXVfzb2h4K9b+lt/97nfF2972tuLqq6+e28nc+973Lh72sIfNXN4i+uDMjVqjBZxzzjnFpz71qbr18R4W72VrcTn66KMbn8sbb7xx8exnP7v1VKbZt7UALxIgQIAAAQIECBAgQIAAgTkICOpnRPzxj39cbLbZZo1SLr/88mKjjTZqvDbJkx133LH42te+Vu96/PHHr1rY+/CHP7z49Kc/Xdf99a9/vdhuu+3q5+O21zt2tBIB8bve9a669t1226049dRT6+ejVs4777xihx12GNjlox/9aPGYxzxm4PW2F/bYY4/ipJNOqjc95znP6Qeb9QtLsLKIaxg+J598cn12eb+pN0y4El+W3PCGN5xw7/G7nXDCCcUTn/jE8TvaY6RAfIl3pzvdaeQ+027cd999iyOOOGLawwb2n3cfHKjgOvRC/K0ec8wxxQc/+MHWs9p7772L+KKsWk455ZSpvvCsjluGx7vc5S7FJZdcUjdl6623Lr75zW/Wz9OVafZNj7NOgAABAgQIECBAgAABAgTmKSCon1FTUD8j4JwOj0B2zz33rEu76U1vWlxxxRXF9a9//fq1YSuHHnpoccABBwxsnjRIjF9VbLrppsV///d/12V86EMfKnbffff6+TKsCOqX4SqszTYI6tfmdata/Z3vfKeIX3p9+MMfLu585zsXF198cbWp8Sio/z+OUaF+A8wTAgQIECBAgAABAgQIECAwRwFB/YyYgvoZAed0+GWXXdb/ZUOE5tVy9tlnFzG8xrgl7r7/7Gc/O7Bb3EGc3pE5sMPvXzj//POL7bffvt58gxvcoPjpT39a3OIWt6hfW4YVQf0yXIW12QZB/dq8btHq3/72t8XNbnaz4pe//GX/JAT1g9fSHfWDJl4hQIAAAQIECBAgQIAAgdUXENTPaC6onxFwjoff8573LP7jP/6jLvFNb3pT8YIXvKB+3rby85//vLjVrW5VXHvttW2b+0MlxN2Vo5YjjzyyeN7znlfvcr/73a8444wz6ufLsrJWg/p3vOMdxe1ud7sVMd7rXvcqbnvb267oWAf9v0BbUB9DPd3oRjf6/52mXNtyyy0bw2tNeXi9u6FvaorWlXw4qVFBffyK6LjjjqvL+bd/+7figQ98YP18La1ME75Ps+9aMtBWAgQIECBAgAABAgQIEFhbAoL6Ga+XoH5GwDke/uIXv7g4/PDD6xL/8i//sogJVEctH/nIR4qYcLRatthii+J73/te9bSYJOyPCRfTev7+7/++OPDAA+sylmVlrQb1Ma70uC9LlsX4utqOtqA+7tBeyaTZ8zYS1I8WnSaoH13S2to6Tfg+zb5rS0FrCRAgQIAAAQIECBAgQGAtCQjqZ7xagvoZAed4+Cc+8YniUY96VF3i5ptvXvzgBz+on7etxB2kb3vb2+pN73vf+4qnPOUp9fNHPvKRxcc//vH6edtK3O196aWX1pvOOuusYuedd66fL8uKoH5ZrsTaa4egfu1ds6rFgvr/kxg17rygvuotHgkQIECAAAECBAgQIECgSwFB/Yz6yxTURyATdx/f+MY3LmJ89Q022GDisxsX4o7bPq6iX/ziF/0JDG9+85sXW2211bjdV7T9mmuuKTbeeOPGMDYRMI6qL5xin1g222yz4oc//GH/8Sc/+Un/tT/8wz8sLr/88qF3Dl900UXFXe961/6+8d8tb3nL/vj0k0xiuyiT+HLiqquuKiJ8SocmWek1DNcwinGuY7iSdJn33cx5qBh1rdYd9cPc0vPN1xd1DaOemOcg2hR98Pa3v32x4YYb5tU3nsdkxrF/TKR8xzvesbje9a7X2D7Lk66D+tXsg7M4pcdW16/6u4m5K1a6xDjzMSFsTJAd/SCGc5p0Doz8b2rU0Dcrbd8ijpvl8yzac/e737244IIL6qZtt912xde//vX6eboyzb7pcdYJECBAgAABAgQIECBAgMBcBXqTb1pmEPiv//qvmL208a8X7K6oxB122KFRzvHHHz+2nNNOO618xjOeUfYmTS17AX19fC8cKnfddddyv/32K3tDuYwt52EPe1h9bJxPL9BoHDNue2Pn3z/p3YlePvrRjy57oWHZCw3r8nthfb9tvXHdy29961tth674tT/90z+t64nz6N0hP7SsCy+8sLHv0572tP6+T37ykxuvf/KTnxxaRm/89Ma+j3/844fuGxvmZfLKV76y7I0d3f+355579us89thjyz/6oz+q29P7sqDv3xsvv799mmvYm5y3fPWrX132AuK6vPCM51Ffb+zqfplxfdP+n/eb/k5T/Pc///M/jfKi7F5QP0UJo3ddiVte4ryu4UEHHVRfw94wTf1qrrzyyjLa2PuVxoBDXNu8P/cC7PLggw8ue1+gNPaP94Le+PzlySefnDd/Rc/j7zS9zrHeG/pmRWVNetA8++BLXvKS2rr3q5vyZz/72dhm/N3f/V19zCMe8Yiy90XIyGN6YXrfuzeUVtkL0xtevZC+7AXk5QEHHDC2nKikF/KXr3nNa8p4P+kFzI339rDvfQlbbrvttuWznvWsgffqqpFHHXVUv/35e2JvuKL6vOI95Atf+EJ1SPmP//iP9baHPOQhjc+O1COOi/ebSZfnPve5dbkPetCDyt4XnK2HzuvzLAqPz7+0z8bzYcs0+w4rw+sECBAgQIAAAQIECBAgQGBWgWLWAtb78V0F9b/+9a/LCJ/SADwNJdL13l3e5bjQf1yIO2572g8iWM0D3LQ96Xrv7t+yN/RMevhM66997Wsb4cxznvOcoeX1xp9v7Pv+97+/v+973vOexusvfOELh5bx1Kc+tbHv0Ucf3brvvE3+/M//vK6396uA8phjjqmfp76x3ruTvt+mSa9hb9z+gaAxLzOe98biLyP0TLcte1C/Erfqgs77GqZfCN3hDnfoh7PpFy2pa7r+/Oc/v9+k//zP/yx7dwI3/NP9qvUIc2ddVjuon3cfnLTvp075e9g555yTbm6s937JUP7xH//x2GsR1yS+qIwv+NqW3l3k5Vve8pZyo402mqisKC9C+wj18yW+FKj6wKjHD33oQ/WhT3/60xvHnHLKKfW2N7/5zY1t8UXQJMv3v//9xudUfImUf5k978+zaNdjH/vYRnt7v/4Z2txp9h1aiA0ECBAgQIAAAQIECBAgQGBGAUH9jIBdBPURfOy4446NEGJUEFNt23vvvYee7bgga9z2quDzzz+/vMlNbjJ12yJMjrBm1uXMM89s1H2Pe9xjaJG98efrfSPsijt4Y/nRj35Uvx5222yzzdAyepPPNvbtDREysO8iTNLA+YY3vGEZv6CornP+GF88xDLJNfznf/7nMu7Ez8uY9PlaCuondQu7RVzDNKgP80lC9+o6HHnkkWWE+9XzcY+9+RviNFa8rGZQv4g+OEnfz3EmDerPPffc8ja3uc3E1yKuVXzBWv0qJa03D8rHXdd0+2c+85m0qP7d++n2YeuTBvVxl3/6q60o7xvf+EajzrYnhxxySMOm+vVIte8iPs+i7Nyy+sVUVW/6OM2+6XHWCRAgQIAAAQIECBAgQIDAPAUE9TNqdhHUP+EJT2gEHxGY3Pe+9y3f/e53l//+7//eH8rg8MMP7w+NkIczJ554YusZjwuyxm2PQnvjdZfbb7/9QNt22223/l3zvUlWywgM4673W9/61gP7/cM//ENr26Z5MYZOiTtWq/OOQKxtmIsYtqM39ne9X9zJnC75MERtQ/T0xoyuj4/6epMVpkX01xdlkgb11blWjxFAV2F7DMFx9dVX99sy7hrGXcH5kB1RTgyfFKFiDKETd9Xe8573bJx3VW88rqWgPm13rA9zW9Q1TIP6tC29eQXKGHbksMMO6w91s88++wz1juMiPI277OMLmSOOOKLcfffdB/aP/jzLslpB/aL64Li+32YzSVAf7zdtX5rGcDPx65ovf/nL/X+ve93rGu9Lcd3iby19X4m719N+EOu9eSbKN77xjeXpp59exi8o4m8wrnPbLy9ieJ50ic+C+Ex45zvfOVBuvB7/YviaX/3qV/VheWCd3lEfO+WfPS9/+cvrY4et5F9A5V8a5WXGec/6eRZtedGLXtQ471G/jJpm32Hn6XUCBAgQIECAAAECBAgQIDCrgKB+RsG2oD7GAF7JvzykaRuu5nOf+1wjfIhjIgSKIRPyJQLaGOM4LTfuAI/gMV/GBVnjtkd5McxMWleEvDHm8e9+97u8urI3aWuZj50cwXnbHekDB4954XGPe1yjHTGmeL586lOfauzzile8orFLDCuUnkvcwZwvEXKl+1RDkqT7LcqkLajvTS5ZfuADHyhj3PIYWuK4444r3/Wud9XNGXcN99prr8b5xJBJbePzR/950pOe1Ni3clhEUB9zMMR47tP8Gzau/UrcFnUN24L6CN2/9KUv1desWsmHaaq873a3u7Xe1Rzj3Ff7xGP8YmSWMeXbgvq//uu/Lvfdd98V/Wu7mzzOdVF9cFzfr5zTx0mC+kMPPbThHF8MHnjggWWMV58vF198cdmb7Lqxf+xbLQ9+8IMb23baaaf+33K1PX2M99QYMz69xrHem9w83a2/ns/7EGPlD1vGBfXxXprW2Zuou/X9vSr/7LPPbuy/+eabN2wW9XkW9cf7RdrWGKpr2DLNvsPK8DoBAgQIECBAgAABAgQIEJhVQFA/o2BbUJ+GA7Os50F9hPH5nd4RrIxbdtlll7GBxbgga9z2K664ojEOcZx3hIWjlriTc8tsEsyYiHHWJR9LOQ/ho/z999+/YZJOqBjbTz311Mb2CP/z5a/+6q8a+8S42umySJO2wHnc5I6jrmH86iAmvEz7axryp+dVrccExun+sb6IoD6vY5LnH/3oR6tmNh6ndVvkNWwL6oeZx7BQbb9CyfttdbIR5MYvPFKrSYYpqY7PH9uC+rTsaddf+tKX5lX0f/myqD44qu8PNOT3L0wS1MewWOm5jxpeJYo94YQTGvvHHfOxxBdr+bmPu17RJzbZZJNGeV/96lf75aX/zTOoj8+gCNvTcx7WB6MN+fvsy172srppi/w8i0ri1yVpO+PLrmHLNPsOK8PrBAgQIECAAAECBAgQIEBgVgFB/YyCqxnUx1AGafAQQ7xEkDhuibGL0+NiqIZ8GRdkjdt+8sknN+qIO0cnaVt+V3oM+9H264C8vaOeR8CVnu8DHvCAgd3jTuRqn3CMMCtd4kuEdKz9mPQ2H0M/QraqjBg2pRpipipnkSZ54JwP3VO1IX0cdQ3zADHGP7/22mvTwwfW//Vf/7U+/8phrQX149wWeQ3zoD5+YTJqya9fNUnwsGOe+MQnNq5P2y9Lhh2bv74aQf0i+2BuN0k/HRfUx1A0Vb+PxwjaL7roopyu8Tz+pqpwfdNNNy3vf//790P6GKc9frnx0Ic+tIzJoR/0oAc1jhv2ZNddd220IeboyJd5BvVRdoTt6XnHLyvalqg3H7v/wgsvrHdd5OdZVBJfdKftjLkPhi3T7DusDK8TIECAAAECBAgQIECAAIFZBQT1Mwq2BfUxhncMWzDtv3TM9AgY8jvqYyiTNHgYF9RVpxaBSQzFUx0bYyPny7gga9z2fBiGScYujjbEEBH5HZrjwq687W3Pb3e729XnG4F7GjrnY8tH6N22pJPNhl1652gM3VN5xmNbyLpIkzyoj+Fhxi2jrmE+DvqoYSKqeuKu7Rj6InWYJACtjm97zEPFtOxp1tuG7In6pnVb5DXMg/pXv/rVbST1a3GNU4O2u9LrnXsrEaCm+5900knp5qnWVyOoX2QfHNX3h0GMC+rf+ta3Nnxj/0mWGN4r7qCfdYlydt5550YbTjvttIFi87+pWYa+icJjWKm0X8WXsvmXmLFf/iVXfKmQLov8PIt68uHNhv3KZtp903OwToAAAQIECBAgQIAAAQIE5ikgqJ9Rsy2oX2kIkw9rkwf1r3nNawYCkr/5m78pJ/kXd4Sn4UoEzekyLsgatz2/s/N973tfWvzI9Zg4M23bqEBlZEHJxhiCIi0zHff7qKOOamyL521LTOKYlhH+1ZLfgXnwwQdXm+rHRZrkgfMkE/GOuobxpU96ru9973vr8xi1kn+ZsYig/phjjik//elPT/wvfkHSNi9CnMe0bou8hnlQP848xoNPr1FMVjpqickz0/3nHdSfe+655QUXXLCif5dddtlA0xfZB0f1/YGG/P6FcUF9fmf5qMlKh9Uxyevxy6QY6z2C7fgCbc899xz4gqy6zp///OcHipx3UB8VxC8Bqjrjsa1v5fNYxKS26bLIz7OoJ79jPybkHbZMs++wMrxOgAABAgQIECBAgAABAgRmFRDUzyi4mkH9U57ylEY4kgYl067ngc64IGvc9vyu+FGhSE7+rGc9q3Fehx12WL7L1M9jmIPU5PDDD6/LyMPauDO1bYkQMi3jgQ98YL3bc5/73Ma2CHryZZEm+TnEJLLjllHXMH4Fkp5rTPI4yZJfu0UE9cMmhp2kffk+07ot8hrmQX0+x0He9jyoHzYha3VcPj54W5ha7Tvuse2O+lkmp22rb5F9cFTfb2tLvDYuqM/nqEjfY4aVOcnrEay///3vLx/zmMeUt7/97Rt/l+nfaNt6/r4e9S0iqI+5FNL6Y9LydLnyyivL9Bdi8aumq666Kt2lXOTnWVR0ySWXNNp4/vnnN+pPn0yzb3qcdQIECBAgQIAAAQIECBAgME8BQf2MmqsZ1N/vfvdrBA9pUDLtej5e77gga9z2fAiUGL950uVVr3pV47wOOOCASQ8dul8+NM0ee+zR3zeGwLnZzW5W13fXu951aBmxYYsttqj3jeGDqnDy7ne/e/36rW51q/4QPnlBizTJA+ezzjorr37g+ahrmJ5n9KW4g3eSJe7wTfveWgvqx7kt8hrmQf1nP/vZkeTX9aB+kX1wVN8fhj4uqI+Jr9O+H+H6rMsHP/jBxntOWv4k66sV1Md8HDGEWtWmG9/4xo05Sd797nfX22Kfpz71qQM0i/w8i8piguyqffF46aWXDrShemGafatjPBIgQIAAAQIECBAgQIAAgXkLCOpnFF3NoD4Pm2JC1Bg2ZiX/Tj311MaZ52Xngeu47WlwHaHIV77ylUb5o57EJIppoHLIIYeM2n3ibWmbYsz6WCLISuvab7/9RpaX3zEeYWoMbbTBBhvU5cSknW1LWv+8TfKg/stf/nJbExqvjbqG2223XX0+0dZJfxHxile8onFc3m8aDZjgSX73b7RlkXfUj3Nb5DXMg/q2kDUlu64H9Yvsg3nfP++881La1vWY2DV9rzjnnHMa++VzBhxxxBGN7dM+iXkVYlLqtM50Pd5z7njHO5bxBcFBBx3U/zLtsY99bGP/tj6U/03NOkZ9dV577713o+50aJv49VHa9rYvofJrMs/Ps2hjDH91/etfv25HTBA+bJlm32FleJ0AAQIECBAgQIAAAQIECMwqIKifUXA1g/rnP//5degQIcg8hoipTj8PTfLAddz2/O7SuDN00uURj3hE47xiWIV5LC94wQsa5cZd9gceeGDjtZjwcNTyL//yL439/+mf/mlgksRh7V2kybyD+nyegEnvDo47ZdNALu83o2zbtuWhYpTdZVC/yGsoqG/2gEX2wfz966tf/Wqz8pZn97nPfRp9Ow/qY16ItO/HxMMrXb73ve817lCvyo3hb+ILgDPOOKOM4WTyJQ/EVzOoj4lrq3bG44Mf/OB+8+Jc0i8y48uFtjkjFvl5VjnFr52ibTEMz7hlmn3HlWU7AQIECBAgQIAAAQIECBBYiYCgfiVqyTGrGdS/+c1vbgQje+21V9KS2VbzICsPXMdtf/GLX9xo2+tf//qJGxTDz6SBzzwmk43KY8zvtNwY0zsNtmK4hmuuuWZkO2NIhPSuzDB/+ctf3ij3+9//fmsZizSZd1CfB+6TTE4bJ/2ABzygYZH3m1aYES8uW1C/yGsoqG92hEX2wfzLwHFDHkXL7nCHOzT6dh7Ux7wQ6ftLjLk+yXLkkUeW8euBCOHjFz3xq464Gz0tK4LlE088cWxx+S8+2u5cz/+m5nVHfYTvd7nLXep2x/vkT3/60zLG6k/PJYbHalsW+XlW1bf11lv321L9oqp6ve1xmn3bjvcaAQIECBAgQIAAAQIECBCYVUBQP6Pgagb1MTRCGoDc5ja3GZigr+104k7MuKsxJsbcddddywgI87uUxwXx47YfffTRjbbFMAa/+c1v2prTeC0mLU3PKe7EHBZ8Nw6c4Emc9w1ucIO6/Je85CWNCQ532223CUopy1122aUuY/vtt2+E0xGUDVsWaTLvoD6fHHKbbbYZe/1+9KMflTFuf3r9rmtB/SKvoaC++ZezyD6Y/zJi3ES83/nOdxr9Ovp4HtTHXflp3487smPs9nHLzjvv3DjuE5/4xMDEqjGszrjlxz/+cePO9WjLKaecMnDYooL6qOjggw9unEuMTZ+eX7yfD5use5GfZxXCL37xi/LnP/95GY/jlmn2HVeW7QQIECBAgAABAgQIECBAYCUCgvqVqCXHrGZQH3Xlwej++++ftKZ9NQ9TYjLVq666qrHzuCB+3Pavfe1rA6HRMccc06ij7Ul8cZCGXRGKz3NJQ/ab3vSmjbomves/nzA1vcN+lP8iTeYd1MedsOmXGnFN8gmH8+sSdwOn1y7Wr2tB/SKvoaC+2aMW2Qef9rSnNfpqjK8+asnv7o++nQf1EQDf4ha3aJQ77j0lJtlO3z/i+F//+tflTjvt1CjnDW94w6jm9bflv/aINrYN5RV3vqdD0cQvBYYtT3/60xvtaAv+02PjS9XrXe969TE77rhjo65qOJz0mGp9kZ9nVR0eCRAgQIAAAQIECBAgQIDAWhIQ1M94tVYzqI+m5mOsR+gTQzAMW2LS2BjiJQ1UY+z2fBkXxI/bHuU985nPbNQTd/yfeeaZeVX957/97W/LV77ylY39o41vetObWvdf6YuvetWrBuqoLCKEnWSJYTKqY/LHj3/84yOLWJTJvIP6OInHP/7xjfOM4SLOPffc1vOLu2HzfhU217WgPk5+UddQUD/YtRbVB1/3utc1+nb01XxC7ao1L3vZywb2jf3zoD72z4d5ibvqh01QfO211w4E8tEHYsn7wiMf+cj+68P+O+644xoBefW+FHNqtC0bbrhhfU4xYe0VV1zRtls5bVAfhTz84Q+vy67aUT2+973vba2nenFRn2dRflzf97znPfW/z3zmM1W1A4/T7DtwsBcIECBAgAABAgQIECBAgMCcBAT1M0KudlAfP8/faqutBoKRuGM0gucY5iCWuDs1QpCb3OQmjX3jjvyLL7544KzHBfHjtkeBMRTDLW95y0Z9cZd2jHd+0UUX9ScU/OUvf1lG8P3Qhz60sV8EOzF5Y4RZ81zyCQ+rAGmSMYurdsQQPhtttNFAeycZ435RJosI6mMSyPxXB9F/3v72t/evbXh897vfLeMXBuldtJVpPF4Xg/pFXcM8nG2bCLTqg/G47777NvrguOFb4tce6bU56aST0uKmWv/Wt77VKCvKjV/D/Mmf/MmK/8XQUzF8Urosqg/+4Ac/GPjFyGabbVbGF3lf+tKX+sOzvOMd7yh33333gfOsDNuC+ni/yufYuNGNbtQP8ONvJZZ4T44hvnbYYYdG2TEOffVF2KGHHtrYFnXGZKsxR0a6nHfeeQNfHFXti8cYqqltiXNN97v3ve9dHnTQQeVrX/va8sILL6wPWUlQf8IJJzTKruq5+c1vPnYOkEV9nsUJPepRj2q0K+YEGLZMs++wMrxOgAABAgQIECBAgAABAgRmFRDUzyi42kF9NPdjH/vYQOhUhSMRHm+yySaNgKLaFqH5hz/84dYzHhfEj9teFXrUUUe11h1tiBA47uas2pM+brzxxq1fIFTlrvQxgrT0btKqznFDX+T1/cVf/MVAu0cN65AevwiTRQT10eYYJzwdJqPyiscI3tLnbevXxaA+XBZxDdd6UN92/ad97Ytf/GLwNpZF9cE99thjbP8d1f62oD4aHr+qyYeNqsqJLy4juK+ep4/HHntsfd4/+clPyvjyMN0e6/Heda973au8//3v3/oFbb7/PvvsU5eZruTv3+lxEdZXy0qC+l/96letX2QOa0tVV/W4iM+zKHua8H2afat2eyRAgAABAgQIECBAgAABAvMWENTPKNpFUB9NjokM73GPewwEO2kAk67H3ZujxhzPg5w8cB23PWWMcY1jHOS0/lHrMflgdfdpWs681mMYibz+448/fqri427bvIxDDjlk4jLmbbKooD5OKGyGhYupQfwC4m//9m8bLnm/mRjo9zvmE19GffnEx9OWme6/Erfq+HlfQ0F9UbYF9eG9iD4YE8TGpNppHx62Hnf755PbDgvqo71xV35+Z/2wsuNvK+YNyZcYmmWSv7uq3L322qv/K6rqeTzGFwMRnOdLlN02VFUcs+eee9a7rySoj4Of97znDbieccYZdbnjVub9eRb1TRO+T7PvuHOxnQABAgQIECBAgAABAgQIrFRAUL9Sud8ft8igvm1iwLS5MQlhDN2w5ZZbDoQkVXgTAf2LXvSi8tJLL00PHVjP7xjPA9dx2/MCr7zyyvLZz352eetb33po2yLojQB83sPd5G3Jx5GOYVtiaKBplvgioTKtHkcFd21lz9Mkn+zyggsuaKuy8do01zCG2IggOZ34sjrvuLP+CU94Qn9C4re85S0NlwhDZ1kWHdSvxC09n3lewzyoP/vss9OqBtZf+tKXNqy7Hvqm6g+zPA4L6uPkF9EHL7/88jKGBLrtbW/bsKzOYfPNNy8PO+ywMubQyId0Gff3fs0115QxwXLMzVGVlz8++tGPHvnFUwxLlo/Tn5YRk89GGencH/kXBKeffvpA34kXor/c+c53HmhbvA9Xy0qD+q985SuNcrfZZpuqyIkf5/l5FpXm4Xv6hUTeqGn2zY/1nAABAgQIECBAgAABAgQIzEtggyioFwRY1rjA1VdfXZx//vlFL2AveuPAF71x7Is73elO/X+9sL7Ts7vsssuKXuhWfOMb3yh6d4wWvS8Wiq233rrohUadtqvLyteKSW989n6f6s0xUPSGxCl6v34oer/kKHpfdnTJtxR1r5VruBRYMzRiUX3whz/8YdELmItLLrmk2HTTTfvvlbvssstc+na0uTdnSNH7Aq3ofbFVbLvttv1/sT7JEsf3Qvv+v96XC/33y94Y98UWW2wxyeEj94lye2P2F71hyPrl9YbcKXpfyI08ZrU3LvPn2WpbqI8AAQIECBAgQIAAAQIE1o+AoH79XGtnSoAAAQIECBAgQIAAAQIECBAgQIAAAQJLKCCoX8KLokkECBAgQIAAAQIECBAgQIAAAQIECBAgsH4EBPXr51o7UwIECBAgQIAAAQIECBAgQIAAAQIECBBYQgFB/RJeFE0iQIAAAQIECBAgQIAAAQIECBAgQIAAgfUjIKhfP9famRIgQIAAAQIECBAgQIAAAQIECBAgQIDAEgoI6pfwomgSAQIECBAgQIAAAQIECBAgQIAAAQIECKwfAUH9+rnWzpQAAQIECBAgQIAAAQIECBAgQIAAAQIEllBAUL+EF0WTCBAgQIAAAQIECBAgQIAAAQIECBAgQGD9CAjq18+1dqYECBAgQIAAAQIECBAgQIAAAQIECBAgsIQCgvolvCiaRIAAAQIECBAgQIAAAQIECBAgQIAAAQLrR0BQv36utTMlQIAAAQIECBAgQIAAAQIECBAgQIAAgSUUENQv4UXRJAIECBAgQIAAAQIECBAgQIAAAQIECBBYPwKC+vVzrZ0pAQIECBAgQIAAAQIECBAgQIAAAQIECCyhgKB+CS+KJhEgQIAAAQIECBAgQIAAAQIECBAgQIDA+hEQ1K+fa+1MCRAgQIAAAQIECBAgQIAAAQIECBAgQGAJBQT1S3hRNIkAAQIECBAgQIAAAQIECBAgQIAAAQIE1o+AoH79XGtnSoAAAQIECBAgQIAAAQIECBAgQIAAAQJLKCCoX8KLokkECBAgQIAAAQIECBAgQIAAAQIECBAgsH4EBPXr51o7UwIECBAgQIAAAQIECBAgQIAAAQIECBBYQgFB/RJeFE0iQIAAAQIECBAgQIAAAQIECBAgQIAAgfUjIKhfP9famRIgQIAAAQIECBAgQIAAAQIECBAgQIDAEgoI6pfwomgSAQIECBAgQIAAAQIECBAgQIAAAQIECKwfAUH9+rnWzpQAAQIECBAgQIAAAQIECBAgQIAAAQIEllBAUL+EF0WTCBAgQIAAAQIECBAgQIAAAQIECBAgQGD9CAjq18+1dqYECBAgQIAAAQIECBAgQIAAAQIECBAgsIQCgvolvCiaRIAAAQIECBAgQIAAAQIECBAgQIAAAQLrR0BQv36utTMlQIAAAQIECBAgQIAAAQIECBAgQIAAgSUUENQv4UXRJAIECBAgQIAAAQIECBAgQIAAAQIECBBYPwKC+vVzrZ0pAQIECBAgQIAAAQIECBAgQIAAAQIECCyhgKB+CS+KJhEgQIAAAQIECBAgQIAAAQIECBAgQIDA+hEQ1K+fa+1MCRAgQIAAAQIECBAgQIAAAQIECBAgQGAJBQT1S3hRNIkAAQIECBAgQIAAAQIECBAgQIAAAQIE1o+AoH79XGtnSoAAAQIECBAgQIAAAQIECBAgQIAAAQJLKPC/AAAA///+BC03AABAAElEQVTt3Qn8PdX8x/Ep/K0pLUpFkbJGkTZRZGkhKksphEK0WAoVkiiih4hkpygJlVDJFilLyZa0SaIoFBUh5j/v+/+feZw5d5Zz7p1zZ/m9zuPx+9353jvLOc9zZvvMzJml0iwlJAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEOhEYCkC9Z24s1AEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBCYCBOppCAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIdChAoL5DfBaNAAIIIIAAAggggAACCCCAAAIIIIAAAggggACBetoAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIdChCo7xCfRSOAAAIIIIAAAggggAACCCCAAAIIIIAAAgggQKCeNoAAAggggAACCCCAAAIIIIAAAggggAACCCCAQIcCBOo7xGfRCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQqKcNIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQoQCB+g7xWTQCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgTqaQMIIIAAAggggAACCCCAAAIIIIAAAggggAACCHQoQKC+Q3wWjQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAgXraAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACHQoQqO8Qn0UjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIECgnjaAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECHAgTqO8Rn0QgggAACCCCAAAIIIIAAAggggAACCCCAAAIIEKinDSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0KEAgfoO8Vk0AggggAACCCCAAAIIIIAAAggggAACCCCAAAIE6mkDCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh0KECgvkN8Fo0AAggggAACCCCAAAIIIIAAAggggAACCCCAAIF62gACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAh0KEKjvEJ9FI4AAAggggAACCCCAAAIIIIAAAggggAACCCBAoJ42gAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAhwIE6jvEZ9EIIIAAAggggAACCCCAAAIIIIAAAggggAACCBCopw0ggAACCCCAAAIIIIAAAggggAACCCCAAAIIINChAIH6DvFZNAIIIIAAAggggAACCCCAAAIIIIAAAggggAACBOppAwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIdChAoL5DfBaNAAIIIIAAAggggAACCCCAAAIIIIAAAggggACBetoAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIdChCo7xCfRSOAAAIIIIAAAggggAACCCCAAAIIIIAAAgggQKCeNoAAAggggAACCCCAAAIIIIAAAggggAACCCCAQIcCBOo7xGfRCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQqKcNIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQoQCB+g7xWTQCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgTqaQMIIIAAAggggAACCCCAAAIIIIAAAggggAACCHQoQKC+Q3wWjQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAgXraAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACHQoQqO8Qn0UjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIECgnjaAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECHAgTqO8Rn0QgggAACCCCAAAIIIIAAAggggAACCCCAAAIIEKifsQ2cc845yfnnn59PvcEGGyRPetKT8r+bBt797ncnt99+e2G0vffeO7n73e9e+K7qj3PPPTfRP5PWXHPNZKeddjJ/8olAsMBFF12UnHXWWfl0ak9qVyQExirw1a9+NfnZz36WF2/ppZdOXvOa1yR3vOMd8+8YQKBK4Oabb06OPfbY5D//+U/VKDN/v9122yUPfehDZ56eCRFAAAEE+iegY+33v//9yYUXXphce+21SZqmyWqrrTb5t9deeyVbb711/zJNjqIJnHfeecl3vvOdfP677757suKKK+Z/M4AAAgggsIQKZAcIpBkEXvKSl6RZk8n/bbnllt5zyQJD+XT2PE4//XTveWy//faFebz85S/3npYRuxH48pe/nO64447dLNxjqS984QsLberss8/2mGqco/S9rsapvthSZcHV9L73vW+hzWt7/PnPf36xGWFpgxU49dRTp9qPvU+fZ/jTn/70YFzYXqZpLINY8x1M44qQUUzjtNcxurZdpuwGq/ROd7pT5X7jhBNOiNDimWWfBbbddttCe/jFL37R5+yStwEJtL39GlDRySoCoxDQlXzSDAInnnhiYcd6j3vcI83ukPea0xFHHFGY1pzMv+IVr/Ca/r///W+6wgorFObxxS9+0WtaRlq8wFVXXZU+4xnPmNTXWmuttfgMeC6RQH2aDqWuPKuU0WoEzjzzzMI21GyHQy661syen5YAgSU9UM/2Mt4+A9v2NyCYxmmvY3SNUabf/e536corr1x63GGOP3QjF2nJEiBQv2TV9yJKG2P7tYh8swwEECgK0PVNdnQ0S7r++uuTVVZZZfLIoplejzE+6lGPMn9WfmaBoOSb3/zm1O8PeMADkiuvvHLqe/eLiy++OHn4wx+ef61uGv70pz8lyy67bP4dA/0QUJcIyyyzTPKPf/xjkqEsUJ9cccUV/cick4vddtst+dSnPpV/m91RH9SdUz7hQAeGVFcDJe5Vtp/znOckJ5988lSellpqqeRXv/pVss4660z9xhcI2AKnnXZa8sxnPtP+qrXh7I76ZJdddmltfm3PiO1lMunyKMb+Hdu2W2u8umo/p/HmGKNdxZhnPAG/Occqk7rVe8973lPIxOqrrz7p4uy2225L/vznPyc//vGPk//5n/8pjMMf4xZ42tOelnzlK1/JC5ndUZ887GEPy/9mAIEQgVjbr5A8MC4CCLQjQKB+Dsf11lsv+elPf5rP4b3vfW+yzz775H+XDdxyyy1Jdjd88q9//avs5+Syyy5L1l577dLfzJfHHHNM8spXvtL8mWy66abJ9773vfxvBvojoPcQZI+55hnqc6A+e6IjyR67zfOa3S2abLHFFvnfYx8YUl2NvS5il08nxKuuumrldlgn1EceeWTsbDD/gQv88Y9/nPQzXFcMtbUXvOAFhVG0nW26sK7jC7XRvia2l8nkPUMx9u/Ytt/qMY3TXsfoGqtMeo/ZN77xjbxxP/e5z02OP/74wjlC/iMDS4wAgfolpqoXUtBY26+FZJ6FIIBAQYBAfYEj7I/99tuvEMzRHZonnXRS7Uy+9KUvJVk3KPk497vf/ZLf/va3+d8+wX695NNezlve8pbk4IMPzufBQH8E2GH2py6ackJdNQmN5/f3ve99yb777psX6I1vfGPytre9Lf97+eWXT37/+98nd7nLXfLvGEBgFgEF6t0Xw+mJvJVWWmmW2fVmGraXcQKfqmBs22/mmMZpV2N0jVWmxzzmMckFF1yQN+4f/vCHib4jLdkCBOqX7Ppvu/Sxtl9t55P5IYBAswCB+majyjHOOOOMZJtttsl/X2211ZKsD8L877IB3bX8wQ9+MP9Jj7fvuuuu+d9bb7118tWvfjX/u2xAd9ldd911+U/nn39+svHGG+d/M9AfAXaY/amLppxQV01C4/n9kY98ZJL1BZsX6Oqrr06e9axnJT/60Y/y7z75yU8m2Xsb8r8ZQGAWAQL1s6gNY5pY+4xY8x2GapxcYkqg3rdlxWor6667bqJuTUzSE1n3vve9zZ98LqECBOqX0IqPVOxY269I2WW2CCBQI0Cgvgan6adbb7010Z2Xdjc22Qs8kjXXXLNyUvVDr3GU1Me97trU5w033DD57q53vWvyl7/8pfJOzssvv7zQd/Jyyy036Z/+Dne4w2T6uv+08VbXOne+850T5UN9MS86qe+03/zmN8lNN92U3P3ud0/uc5/7NHYB0JRHXRz529/+ljzwgQ/07ttxURZt7TBnKWOTW4zf//rXv07adPYqjORBD3pQovcnzJPUt7/ai9q3uoSK2WbbqKu///3vk3cQ3POe96zdDsxjsuhp5aKnfm6++ebJOqb1dt60qPWvLJ96l8gGG2yQ//TQhz400Xs/3LvsdfFTF0HbSHqHiNZhbd/VJ22ToYK7Gj97SXly//vfP1l66aXnykaM7e5cGSqZOOa60/Z2qST7lV+1Hajvct2xC9nG9lLzm2XfFqs9m/VU/c6vscYajfuvtgxsVw23Nd+Y65Sb56q/x9heFVz17Ue8L221zXZl13VbbbUv7SSWk+brBup13qJtTUiaZXsZcztgtpldHdv42sVaD32Xr3iBzv3NvsWebt5Afaz6NXVr8jzv+Zxd5jaHTT67aoOxtl2zHre2tU1us46YFwIIzChQfLcsf4UKPP7xj08z+vxfdod85SwuvfTSfDxNk/VbOxn3ec97XuH7M888s3IeH/nIRwrj7rjjjpXj6odzzjknfdGLXpRmL7lNswB9Pm22400f+9jHpnvvvXeaBeFq5zHPj9kOND3kkENS5TN7OU4hDzLIAq/pQx7ykHSPPfZIsztNKheVdU2RZv2lT/7tvPPOk/GyF5+mj370o/MyZcHcdNttt02z/vpL57NIi2OPPXaSV7d9ZF1p5OVQeb7zne/keQ0p46te9arCfA444IB8Pk0DWfdKhWnPO++8ySSHH354/n3Wl2Zpuzj00EPzcfbaa6/JdNkBaJp1G5Jmgfm8LlS3Kmv2WG+q8bKuHpqylf+eXbRKs66c0vve976T9mHWr3vd617pVlttlR511FFpdtCd/uQnP8nzIsvsSZR8HiEDs9SVPX8tV+0uC6imWUA1N8iC9ZN1LHufRPrrX//anmTu4f333z8ve/ZUT3rjjTc2zvMNb3hDPo0cswBi5TRZ4HpSb9nFrzQ7OM/LpPU1C2Cl2ZM/qeaX3YleOQ/3h0Wuf+6y7b+zp5ry8qhtZf3RT37O7m4rlFW/XXTRRfaklcP2epF1gTYZLzvITrVOZ09AFZan+Wq75e4rzHokX9Pm9ant9vrrr59mLxurXL77Qxvb3QMPPDBvL2bbG/qZ9cfrZq3wd1vrju0fa7tUyHjAH6oLuz41HLI91KLmXXdUD27dvfOd76wthbaxOkaxp3vpS1+azrK9DNm3le2/22jPZYVVGbVeZd0RptnFs0I9abuXvVMmfd3rXje1rZzFoGz57ndtzLetdcrNW8jf87TXNtuq8jyLaVN7rTvW7FtbndWgqb5ncXXnOU870bz60FbcMrl/6/zObEPdbcxmm22W/2bOazR9U/vTPqSuDWoebW0H7H1rH45tVDafFGs99Fm2GUf7+Te/+c1pdnNGYd+iv1Xf2XvAJqPq/ME+Rqg7Fzbzbqt+zfz0Oev+0Mwj1nlJH9vgvNsumdnlauO4tY1tsqlLPhFAoB8CST+yMdxcvPWtby3sYF/+8pdXFkYBUntn/JnPfGYybtbFQuH7rO/kynk8//nPL4z7oQ99qHTcf/7zn6l2mnbg0F62PZzdlZ+eeOKJpfOZ9cvsim569NFHpwqu2suqG1YQUEH9svTMZz4zn0/2NED6iU98Iv/bnedTn/rUwiy6sNDJvZuvsr+/+MUv5nkNKaMuvNjzy+4kSLO7c/J5VQ3oQMw9aLzkkksmo2fdfBTmefbZZ0/Nxr6olD0Jkmbdh6TrrLNOYTo7X2ZYwUr7osTUjP//i+wdDlMBEzMP+zO7AyU9/fTTC8v9+Mc/XjXb2u9nqSvNMHs6ZRKgt/NVNZzdGZ1mXV7V5iPkx6c85SmFsvsc2LsnA1VBaF0M9NluqKzZXYXpu9/97tqsd7H+VWUoe0Ij1fbOricdcJukix72bwpM+iR7vdAFJp0k2hcR7Xnaw+bg/Fe/+lWa3dlfWLY9nhnWBc261OZ29+lPf3pjfky+qj6zl6aWZrftdcf2j7FdKi2E55dqC66Pb6C+rXUne+pvcrHezocCPVl/yZWlePvb3z6Vb20bZtlehuzb7P13m+3ZLWh2Z2q60UYbTZXRNjLDuuiqsps0i4GZtu5znvm2vU7V5bPqtzbaa5ttVfmcxdS3vQ6hrc5qUFXH5vtZXM20bbQTzasPbcWUqepT5ytmO9L0qeNzJd/2p/nZbVDTtr0dsPetXR7bqGw+KeY+w2f5Zhzfc5nsHXOpe9xZdzzfdv2a/M6zPzTziHVe0qc22Na2S2Z2udo4bp1nm2zqkE8EEOiXAIH6OetDdyPbB1/Zo42Vc9RdqGZcBaXNyfq1116bf6/fdWdyVcpePlsYN3uUbmrUa665Js36YC6MZ5Zb97nbbrtNzWvWL9ygb91y3d90l4yb7APXO93pTqmeCHCnM3/rwodJXVnMssMMKaOC0lm3QQUDu9ym/O7n17/+9cI0ClSY5NZZU6Be3m4eTB2UfSowpMB+VdIdxvbd22XzqPtukYF63XF+t7vdrWBZlzfzm06qdKA3b4p1QKy7S1VPJr++n8cff3xpkbpa/0ozk32pi6N2mfQUxH//+9989M997nOF33WBRXfGNyX7gFt+PkF3k49jjjlm8vSI+bvpM3s3SmV23HW4aV727+52N1agPsa6Y/urTG1ulyqxPX+YNVDf9rqjC7K6oGvX+SMe8YhJsMstSvaSw6lt8XOf+9zJaLH3bfZ+rM32bJdRT2OtvPLKBQvbpWxYFy/NHZCzGNjLrxqedb4x1qmqPFZ932Z7bautKq+zmPoeiw2hrc5qUFXP5vtZXDVtm+1E8+u6rSgPdWneQH3I+U6M7YC9b+3y2KbO2P4t1j7DXkbT8HHHHTfTcbTZ71QF6mPUr8oy7/7QeMQ6L+lLG2x722WXS3U/73HrrNtkU398IoBA/wQI1M9ZJ//+979T3W1ldrA6mSvrhkJ3ctonybrb0k46YTbz0GdZVxlZX92FcbI+u+1Z5MPPfvazC+NpfhtuuGGqQGb2ssTJnc1HHnnkpMsZe5kaPumkk/L5zDqgAK87X3WhoS5Lzj333FR3j+rxdp3klN11qm453GSfOLnz1oGsCS7q0dKsL+188q4s5Czvj370o1MW+l7/1HXPbbfdluc1tIzuTlnd1TQldWVg+9l3ebsHuD6Bentesn7/+9+f6i7affbZJ1Wd279ruOoOZXW/5Aa+tS7tueeeqZ46+P73vz95hH3TTTedmqdZhkxnSaF1lfUHmT784Q+fyseWW245uWs+69c8VTBVT9tkfdlOjac7VedNMQ6IFbB286snN9ROf/zjH0+2SbowedBBB01dKNtkk01Ki9TV+leamexL1ZFpL/pUF0t20vroPgX0gQ98wB6ldNg94DbL0BMHWi/11IEuRO2+++6F5ZvxzKe6udFd9to2arnbb7/91PjaV5Sltre7etJATzjV/VOXVyqbug8yZTCf6tLM3RfGWneq/JWXebZLZc6h380aqI+x7qhNmfoxn7qjz07af7rbbgWbzAWr0O2l5h26b9M0bbdnzVNJx2xlNzKomzo9oaiLFPp32GGHFY7t5KXjCx2bzWLwf0uv/3+W+cZap+pzOv1r2+21jbaqXM5i2tReTVswx5p9bquzGkzXcPGbWVw1h7bbiebZZVvR8uvSySefPDne1/HpaqutVtj+6ilrcz5gH782tb+y851Y24Gqfesij23qfO3fYq2H9jKahnVnutvFkepL3czqQq/OfbP3IaXrrbdeoS2Y/bE+ywL1seq3jf2hMYlxXqJ596UNtr3tqiqX2oCWFXo+Pes22dQfnwgg0D8BAvUt1Ml2221X2OGq7zg3nXXWWYVxFOyyk7qpsXfUusvSTQqY2eOYbhPs8b71rW8VxtH4OuHU44Bu0kmG+o6356k79nVAME964hOfWJhn9uLGVP0vlyUFB9XXtZ0HDau/aDuVHbguu+yyqe6A1byzF/BOAsQf+9jH8sn6YKGDILts6vO2KoWW8Ze//GVh3gpsZy8nrpp9esstt6S6Q9jkR0FBO5A2a6BeJx+6AOMmtS/3YNS9kGKmUd+XJl/6VLC07M5heb72ta8tjGums090zHxDPn3rSt1bmWXqUwfh6t/fvjPbLFf14b6nQBfsrip5EsZM4/MZ44BYJwd2uXRRpOruf3WZ4V5Y0QU4O/Vh/bPzI3M9yWTKqOGyC6J6p4AZR591T0mZ+ZcdcGv9+sEPfmBGyT/dLtDMsh784AenWqfdpP5qzTj6VL514ddNMba77jLK/ta7Dtz3U6y00kqltrHWnTL/NrZLZeUN/W6WQH3Mdcd9zF4Bl5///Od5sV784hcX2psuhJe1Y9/tpWYcum/TNLHa87ve9a5C+bTfPDi7YGe6ndCyTbriiivS5ZdfvjC+xjUpxMBM4/MZMt9Y65RPPs04sdprW21V+QwxbWqvuhnBPtYcQlsNNTB12/QZ4hqrnSiPXbWVJh/7d/cGj6ruKpvaX9n5TqztQNm+ddHHNrZh3XCs9bBume5vu+yyS2F/oa4Wy947p/PsnXbaqTCuOc4rC9THqt8294cxzkvk24c2GGPbVVauNo5bQ7bJbvvlbwQQ6JcAgfoW6kNXx80OVp9uEF6L0Ms/7XHc/rrdLkkU/HfTS17yksI81AeenRSMd+/MV/C1KeluWDtv7h12TdPbv+sA0u2+pCz4ZE+jYOCKK65YyIPu4LVT2YGrLlxUpT5YKG8hO8zQMmr+G2+8ccGtrr9wPY5p17N5MZTmozRroP4LX/jC/82g5H/dXaKXytrLvfDCCwtjqusnO4CqcZvemVB2Z8MiAvU33XTTVP/tCqTWJd2lvYbzglC9vHCeFOOAWHeT2vVU9b4Ik283oK2LFSb1Zf0z+dGnXupll2/zzTe3f86HdVeKPZ6Gv/vd7+a/lw2UHXDbgRx7Gm3v3CcXtAx3n2Cm0QUgPT1l58ndpsba7po8VH2qbbsXonQSX/ZC0JjrTpn/vNulqjKHfh8aqI+97vzhD39IdSHFbk/qAk2BapnZ32v4iCOOKC1yzH1bzPbsXlTSU2Z16bOf/WzBRE8bmBRiYKbx+fSdb8x1yiefGidme22rrSqfvqYaN+RYbChtNdRA4/skX9eY7UT57Kqt+BiZceYJ1Ned78TcDpTtWxd5bGPsmj5jrodNyza/68Yn9/y3yspM475vTPtcN1Afs37b3B/GOC+RU9dtMNa2q6xcbRy3+m6TTRvkEwEE+itAoL6FunHvbC4LAOluSXMCrK5ytCG1kwIe9h2quvPZvZvVfhxdd7mZx27NfNwAk5ajHXxTUt/EJm/61GPhsyb14aYr/09+8pNTPS7/hCc8wWtWj33sYwt5UBcbdnJPnNyug+xxNdwHC+UjZIcZWkbN3w2u6g72qqQuOOx6dp/8mCVQ/7jHPa5qcfn3bt3q8VQ7uWXQxaayu9PtaXT3tnn815RpEYF69eFulqdP3W3ps465T8PoLlYd/M2aYhwQu/2zq591XWipSpdffvmkz3d1S3TDDTcURuvL+mcypSCk+34PvZC6Krkn1DqgrkvuAbeC13XJrT/3hXDutOof3G537roba7vr5sP+W+vozjvvXMiX8lh1kS3muuP6t7Fdsss6z3BooH4R685pp502VW+6kOVeMFc3dFXb4pj7tljtWfsNez1SUEXbsbqkF1YaF13g2GyzzSZP8GmaEIO6Zbi/+c435jrl5qnq79jttY22qrz7mmrckGOxobTVUAON75N8XWO3E+W1i7biY2TGcY8rfO+obzrfibkdcPetiz62MXZNn7HWw6bl2r+7F3X18l3tP+pS2cVxN1Afq37b3h+6x7VuOcoctt1228I++aKLLpoares2GGvb5ZarreNW323yFDRfIIBA7wQI1LdUJauuumq+s1HA3d45u33L6ySgLNkvm9XJpH2HpbrQsE8wyw6W9DiuPU5T8MfkQRt1+65ndU+yyKQuKdw7w9U/sp3cE6cXvehF9s9Tw32xCNlhhpZRhVaQ2H73geq/7OBIAVc94m/ah15a4waKZwnUq8umpuQ+3qmDTjvtsMMOeb6Uv7Jun+zxzfDTnva0wnSLCNS73TQdeOCBJju1nwoUu/2TNgWI6mYY44BYXZjYbUR1oS6I3vSmN036bC7rGqIqj31Z/0z+vva1rxXaSlUXTGZ8PZli1hV96i5x92KEGVef7gG3gp51Sdsve/6vf/3r60ZPX/aylxXGP+WUU2rH9/nRZ7tbN58DDjigkCeVR+9lqEox1x3Xv43tUlU5Qr8PDdQvat3ZY489purPbpOrrLLKVBd0dtlj79vsZfkM+7Rn9flql1FBAp+keeuOTTeFGLjT1v3tO9+Y61Rd/uzfFtFe522ryq+vqcad5VhM0/mmLtqq8hZi4FsW33kuop0oz4tuK75OGm/WQH3T+U7M7YC7bx3CsY1vnfish77z0njuO4h8nk7XhfA111yzsF9yz+Fi1W/b+8MY5yVy7boNxtp2ueVq67jVd5ssWxICCPRbgEB9S/XjvqTT7tP12GOPLeyE9XdZ0stW7ZNIu+sJ3aVo/6aX+LlJ49vj6G5fveTP55/dd7nmUdfXubtc378VVFa3J9rp6QBGd2O6Bygm/9/+9rcLs3VPnJpeyNkXi5AdZmgZDZDbJ6IO6tz0zne+s9A2yg4IZgnU60CvKbknTm6XTe4LhRVU9UnuCywXEah3nw7QC0J9k/tEw+mnn+476dR4sQ6I3fKZ9VGfK6ywwmSd1YtOr7vuuqk82V/0Zf0zeXLvSFdbr0t6jN59hFnrUFVyD7iPP/74qlEn37/iFa8orI96qqQu7bvvvoXxQwL182x3q/LkPgWj9vH85z+/avTJ927banPdcf3b2C7VFibgx9BA/aLWHb2zxO1SyazvumCn7vjq0iL2bWXLn6c9uxeXtF7Nk0IMQpbjO9+Y65RvfhfRXudtqyqLr6nGnfVYTNPaqU9tVfkKMbDLUTfsO89FtBPlc9Ftpc7G/W3WQH3T+U7M7YC7b+3TsY3rW/X3POth1TzLvtfNcWYfqs8mKzMP9yY9N1Afq37b3h/GOi/pug3G2na55WrruNV3m2zaH58IINBfAQL1LdWN2//3kUcemc/ZPejXVfyydMkllxR28ltssUU+2p577ln4TY9iuWnXXXctjGMfMIQOu4Fyd1k+f2tn8ZnPfCbVnc+rr756UN7c5buG6qajLvXFImSHGVpGU373/QZ63NLtruBhD3tYwf/iiy82k+efswTqfYLN7kuQ3EC9e6f5pZdemuepbuDoo48ulGkRgXo3r2Uv0a3Ks3vBou59AlXzMN/HOiDWBbr111+/4Fq27dA7BfSyWV1c1EmQm/qy/ilfelJAd8S75dCTT3X/3PHVlZe7Xplyuwfcbhs345lPN1B/6qmnmp9KP913nNQF6tvc7pZlRt3uuBcx9ISX21WbO23Mdcf1b2O75OZ/1r9DA/WLXHd0Q4Fbl2r3ZRd73fIvYt+mZbbZnt33/NjHaW75fP4OMfCZnxnHd74x1ymTl6bPRbXXedqqyuBrqnFnPRbrc1sNNdD4PsnXdVHtRHleZFvxMTLjzBqobzrfibkdcPetXR7bGMemzzbXw6Zl2b+r61H7uFEvIPVJ7rmBG6iPVb9t7w9jnZd03QZjbbvccrV13Oq7TfZpm4yDAALdChCob8nf7ZpG3XkoqQucZZZZJt95r7POOrVLtPtRVnc0//jHPybjq79ocwCgO1vLuqFQ4MyMM++nLjzMkz7/+c9P9QkdkqemQP35559fm72+WITsMN2Tw6YyGgC1BfdlpbafnmKw7R/zmMeYSQufswTq7eUUZmb90RSoVzckdv58n+bQRSB7ukUE6t0nQNTHo29SFzJ2fl/3utf5Tjo1XqwDYi1I77541rOeNfWCXzvv9vBaa62V/uxnPyvksS/rnzLlXtCx8x46fMYZZxTKaf5wD7i/+c1vmp9KP2MF6tve7rqZ10u+3aev9O4UBaObUsx1x/VvY7vUVB7f30MD9Ytcd/7+97+Xvtj4He94R2PxFrFva7s96yXe9jqvfcg8KcQgZDm+8425Tvnmd1HtdZ62qrL4mmrcWY7F+t5WQw00vk/ydV1UO1GeF9lWfIzMOLMG6pvOBWJuB9x9a1fHNsaw6bPt9bBpefbv9vm79jM69/JJesLc3i+5gfpY9dv2/jDWeUnXbTDWtsstV1vHrb7bZJ+2yTgIINCtAIH6Fv3tYLr6rFfShtfeAe+99961S3SvrOugSH2j6g5WMx9141CW3J2kXmCr7jZm+df02HvZ8s13Z555ZqqX3Zr8up8qy/3vf/9UBwmHHnro5GDm6U9/emF8d4flnjj98Ic/NIsr/eyLRcgOM7SMdsHVb6TtrL4STXK7zfjABz5gfip8dhWod7tfUDDQJ+luSLvMiwjU2+u4ln3BBRf4ZHUyjnvBwicYVjVzt33//Oc/rxo1/14veLa9yl7alI+cDVx99dXpYYcdluqxW/fFvfZ8NKyLLfYLTt38dbUtUnncu5zcvIf8rW1WWQo94I4RqI+x3bXL+tvf/ja138UiN3Wvdtlll9mjVQ7HXHdC/ZVJd31sulOwsmANP4QG6he57rzyla8sbBPMuqCXXf/0pz+tLVnsfVuM9uy+G6JqX1hbcOvHEANrssZB3/nGXKcaM/n/Iyyqvc7TVpVVX1ONG3osNoS2Gmqg8X2Sr+ui2onyvMi24mNkxpk1UN90vhNzOxC6b41xbGP8mj5jrIdNy7R/d59c9n3i9qCDDirsh91Afaz6bXt/6K7jbZ2XdN0G3XK1dT4TWi61NZ/jVt9tst12GUYAgX4KEKhvsV722Wefws5WdwYffPDBhe/cF2m6iz/55JML46tfZPeN7x/72MfcySZ/77XXXoVp5+lao3QBHl8qmOPeIa2Tf3V/o5Pi733ve+lf//rXqTmpmx8TJNDnvIH6PliokCE7zNCTQxvxyiuvLFzM0UtAtWx11aEXxxpbdQGirkDKUleB+m222SbPn/J52mmnlWVv6jv3AsQiAvXuHSi6e8c3bbXVVoVyVq3HPvNzDxx9Lm7oSQrTDvTZFKi386HubVRWXQCq6sZKd1abJ336sv7JxS6zhtWNzBvf+Eavf1tuuWVhel2w0DbOTaEH3G2fzMba7ppyqv7dIIOCue522oxf9hlz3Qn1V/58TnjKyhH6XWigflHrji6sueuG/bfq+7bbbqssbsx9W6z2rL6e7TL6dPFTCZD9EGJQNx/3N9/5xlyn3DxV/b2I9jpvW1XefU01bsix2FDaaqiBxvdJvq6LaCfK76Lbio+RGcfdh/7tb38zPxU+Q9qfJoy5HQjdt7Z9bFOAqfkj1npYs8ipn9z3UPk+saX3+9j7JTdQH6t+294fxjov6boNxtp2hZZLDc7nuNV3mzzVgPkCAQR6J0CgvsUq0R159s5W/Q7bAWgFSW+99dbaJd54442Fu1f1otADDzywMN9rrrmmdB7ve9/7CuNp2kWnj370o4U83PWud01POumkxmy4dwy4j1eGHrj2wUKFDtlhhpbRRbXbmtrhN77xjUlfnXabVJcmVamrQL3b/7bu4vZJ7h3iiwjU77fffoX2fcQRR/hkdTKOur2y68KnP8KqmbtB/6ZHozUfvbvAXn5doL4uQKd56U4h9wVXmrfpCqgv6597gK0T5ZCkO4JsMw2rCyM3hR5wt30yG2u7q3Kq+zb3BFQOeqlwSIq57oT6K98+Jzwh5asaNzRQv4h15/rrr09XWWWVQtted911U+2v7faubXNVirlvi9We1dezXT71PeuTjjnmmFR3S+qGAz0Vae5wDTHwWY4Zx3e+Mdcpk5emz9jttY22qjL4mmrckGOxobTVUAON75N8XWO3E+W1i7biY2TGiRWoj7kdCN23tn1sY+yaPmOth03LtX93A+5NLwE2026++eaF/ZIbqI9Vv23vD2Odl3TdBmNtu0LLpfbic9zqu0027Y9PBBDorwCB+hbrRneK2y9m23///Qsnvro70ydtsskm+U5bB3b2TlwB7aqkx/7sk9CVV145rbpjw56H8q2uaPTCGgXftPPw7c7Ano+G3Zeu6NG6pvTHP/6xcDe4ynD22WcXJgs5cdKEfbBQPkJ2mKFl1Pzt9KlPfapQ/wooHOQ8UvnlL3/ZnqQw3FWg/sMf/nAh3+pew7yboZBB6w8Fme3uoNRmFhGo/9CHPlTIqx6BvP32262clQ/qpVL2uqm8V11wK59D8Vv3Dpuml5H+5je/KSxfeXED9a95zWsm2xptNxSwUz+vdemWW25JV1xxxcJ8zXrbh/VPbUhPltjuekIpNG244YaFeegJFa3Xdgo94G77ZDbWdldl3G233Qrll6e2K6Ep5roT6q+8+5zwhJaxbPzQQP0i1h13+6H1WPthXXi01xdtp8w67ZYt5r4tVnt2n7DR+370To6mtPHGGxdczLsqQgyalmH/7jvfmOuUnZ+64djttY22qvz7mmrckGOxobTVUAON75N8XWO3E+W1i7biY2TGiRWoj7kdCN23tn1sY+yaPmOth03LtX/XU7L2/vNBD3pQ4/nBtddem+p9dPZ0bqA+Vv22vT901782zkvk23UbjLXtCi2XLHyOW323yZofCQEE+i1AoL7l+rGD7O5L93zvvnVfLGP3D113h9sf/vCHqR1+3fim6G9729sKBwl6+a1PgN9Mb39usMEGhXm95z3vsX8uHXbvFtABi9tFUMiJkxbSBwvlQ13P2AFl3dVclULL6M5HgVP7xcV6sZF9YqA7KLUDr0pdBeqV73vf+96FdnPUUUdVZXPy/bbbblsYX21m3kC9T13ppal2fWq5n/jEJ2rzqh/du8+1nZgnveAFLyiUX8HUuuTe6aN8u4F696mWU045pW6Wk9/cpwTMnf19WP9OPPHEgtHSSy8908UR9yRJdm6XR6EH3G2fzMba7h5yyCEFQ5Vd70jRuhKaYq47of7Ku88JT2gZy8YPDdTHXnc+8pGPTNWp1hUlXXR0L0zpwmlZd2k+20vjEbpvi9Weta9ZdtllC+VvOi7TU0L2MZim/+c//zkpWoiBsfD59J1vzHXKJ58aJ2Z7bautKp++pho3pL0Opa2GGmh8n+TrGrOdKJ9dtRUfIzOOfTyufWnVeVZI+9O8Y24HQvetbR/bGLumz1jrYdNy7d+1r7dv1lMdH3fccfYoU8O6oUrj2f/cQH2s+m17fxjjvERgXbfBWNuu0HLJwue41XebrPmREECg3wIE6luuH3WLYO9w7WHtbH2Sgl32dPaw/cLGsnm5feLrBFOPt1UlvTRWXfLYy1Bf+7Mmd8ez9dZb187qhBNOSBVAs5evYfXVb6fQA1dN27WFyb/dZ79esqv+nsvSLGV05/PiF794ytLYvva1r3VHL/zdVaBemdAFHZNPfepgt+wdCzqxec5znlMY10w3b6Be+fCpK9dYd6Cfd955mnwqqc929Ydu8mg+3/ve906NG/KFugcy8zKfVS+APuCAA6bG1TRuoN5dX3Sh54YbbqjM1llnnVW4aKH1WC++Nsmd36K3RW7XSE984hNN1oI+9cTR3e52t4Kh+3SUu91r6ru97ZNZd/ltbHfVtY1pW+ZT3Ws1dYtUhxtr3XHL3+SvPPqc8NSVxfe30EC95htr3bn88ssL2zjV6w477FAoysUXXzx1TFDVZZrP9lIzD923ufXZRns2hXRfQq676k1XNmYc86lun9wAkPJmJ18DexqfYd/5xlqnfPJoxonRXttuq8qrr2lIex1SWw0xMHXr8+nrGqOdKH9dthUfHzNOrEC95h9rO+C276Z9a9vHNsau6dPNZ5v7jKZl27/vuOOOheMmXej+yU9+Yo+SD+tObff8W/tkN1CvCWLVb5v7wxjnJSq7W7ddtMEY267QcsnC97jVd5usecpTx/vmn7rNJSGAQD8ECNS3XA/nnHNOYSdtAhzaWfsm3dXmdtmg+fj0ca/uKtZcc82pPOhKty4UmDuqFTzQjscNQOkRvCuuuMI3q1Pjvetd75patvqJVt/7dlLfz+6Bh7HSp+5itVPIiZOZrmsLkw+3L+BHPepR6aGHHpq+9a1vTS+99FIzWnAwI5/QGvjud7875W9cZV6XugzUq5uSspeUqssBvfBPLyLec8890zXWWKOyfG0E6n3qSl1ELLfccoV86MKC+qPUCaPuZlB5dMHNDRarLvRSVwWA5km/+93vpu7cUd51ofAHP/hBetVVV03uMNt+++0L+TRtQZ9uoP6Xv/xlIfCucdZaa63005/+dKEbHF1oUhcy97znPQvz1p3Wdupy/VNXP+4FQJ8nH+z828PunUJ6qsJed0MPuNs+mW17u6vtiC4q2u1FwzqQ1wvSdPei1smmf+57GGKtO6H+qlvfEx67HcwyPEugPsa6o33/RhttVKhTBalVJ25yXzJn6t4dz2d7qWlC999tt2c739r2uk8C6cXIClhcffXVk1Flpe7KHvGIRxS81CWYG3TxNbDz4DPsO99Y65RPHs04bbfXGG1VefU1DWmvQ2qrIQambn0+fV3bbifKW9dtxcfHjBMzUB9rOxC6b2372MbYNX3GXA+blm3/rpfauk/T6zxbXXyafa32M3py3j1GNcdbZYH6WPXb5v4wxnmJbPvQBmNsu0LLJQvf41bfbbLmqRsxTNvTp97FQ0IAgX4IEKhvuR6007OvZJqNX1PXFG423A2n5uN7R6j6IXcfvzP5ULDf7Vfa/KZpTjvtNDcrQX/rDlxdlDDzNJ8yWX/99dPNNtus9EKCGc987r777oXlhpw42RN2aWHy8ZSnPGXKw5RTwXqTZi2jmd58rr322lPLe/SjH21+rvzsMlCvTOlCwgMe8ICpvBurps+mR0wrC2794FtXxx57bGU+dZBeFuRU/pdffvm5LoRZWZ3cCdtkUve7G6jXvA8//PDScikwrScHlP+yeer9Fnbg2uSzq/XP7T5MAbaqx8xNXus+dceJW+5Xv/rV+SShB9xtn8y2vd3VhRi3vLP8rRNUN8VYd0L9lSffEx43/6F/zxKo1zLaXnd0Yd6tQ9PljVsmBb+0z7DHV7dqv/71rwuj+m4vQ/dtbbfnQqazP/RkYtUxki7CKnBvl90M6z0wbvI1cKdr+jtkvjHWqab8ub+32V5jtFXl19c0pL0Oqa2GGLj1W/e3r6vm0WY70fy6bivKg2+KGahXHmJsB0L3rW0f2/jaxl4PffOh8dRXvdtFptmHuDe4mO/tz7JAveYbo3413zb3h3pCzy5L6HDZeUlf2mDb267QcqmufI9bQ7bJbryJQL2kSQj0Q4BAfYR60CN37s6p6oS4avFl/S2+4x3vqBp96nu9JGbdddedyoebL/O3AlltBDqVET02VXWia5Znf+6yyy6Tu/3t73SybHexEHLi5GJ0aWE8yh5vVHl33nnnPLvzlDGfSTZQdjfk0UcfbY9SOtx1oF6ZUlBLF6TstuAOK8CirmP22GOPwnjzXmTS8tV2fepK4+oli3rngJu/qr/1dIC5Y1PTz5t017heAl21PPt7ddXivuiq7IBYedILoO1pm4bVHYq5U6isTIte//REg/tUkb2eleWx6TvN84EPfGDBRRctzEuPQw+4Y5zMtrndjRmol3Xb606ov/Lge8KjcedJswbqtcy21h093WP3s651Wk/b1CU9gedecNS7NuyXZ/tuL2fZt7XZnsvKqSeP3Dvrq7Z1Op7Ru3zKkq9B2bR134XOt+11qi5vVb+10V5jtVXl2dc0tL0Opa2GGFTVcdn3vq5m2jbaiebVh7ZiyuTzGTtQrzy0vR0I3bfGOLbxsdU4sddD33xoPJ3z+5wH6wlbdUtq73uqAvWab9v1q3kqtbU/jHFe0qc22Na2S+ah5dI0vsetIdtkAvWSJSHQTwEC9RHqxe3zTY+36WQ9JCmgZ++4NVwVWKuar154pq4w6roLUYBed4Zed911VbOZ6Xt1n+P21WeXRy9k0wtB7b693ZPmc889N1+2+zLMSy65JP/NZ6BLC+Xv1FNPnXQjYhtoWAdpJs1bRjMfPXppL0cHiz7tb5ZA/YUXXmgWW/npe2BhZqC7OfWyTnV586QnPWnSDZTuQtHwQQcdlF5wwQWTUd38qtupNpJPXZnlqP/yl770pVMvw7X9Vce68DZvdzdmmfan+oTXC6Pvc5/7FOrcLF93uquvf/WV/9nPfrYwTt32RCcDurBg5lP2qSdn3vzmNxcCd3be7OFFrn96MsPNr/tyajtvvsNlF8DOOOOMyeTuAXfTevH617++kEe1ubqkOrbLVPWi37a2u8ccc0xhefayQ4bL7qg35Wxz3Qn1Vx5Ct0sm36GfN998cyHgreB3yNMdbaw7ZV3e6AVpTansZcLqCstOPtvLWfdtbbVnO7/28K233prqZX56WqiqXes45bLLLrMnmxr2MZiayOOL0Pm2uU55ZK90lHnba8y2qgz7mM7SXofSVn0NSiu35ksfV3vyeduJ5tWHtmKXqWl40003zbczuiHEvhnJnnaW9mdP3+Z2IHTfGuvYxi5f3XDs9bBu2e5vOhaVn3uRXPsandM8+9nPnhwL6EYqe/+jYHddarN+7eW0tT9s+7ykb22wjW2X3EPLpWlCjlt9t8luoH7eG5uUTxICCLQjsJRmk+0gSCMXyIIFSfaSuCS7Up9kd4Em2R2nSdbNyORfFqyPVvrsTtskO3Ca/Mt23knWLUuS9fuaZC+qjLbMphl3ZaF8ySLrxy/JgjUTgyzYmWQHcU1ZDvo9u/iRZHc+5tNkF0ySLPCd/z2Wgac+9anJ1772tbw4Wd/BySMf+cj873kHQuvq+uuvT7ID8yTr6z3JLo4k2QWySXvP+nmfNyte0//+979PsosYyZVXXpmstNJKk3V7k002SbILhV7Tl42UnRBMthsqk4azk8sk6/twMu+sK6ske7y3bLLa77pc/2ozNqIf+7jdrePtet2py1uffuvzuhO6vQxxXUR71jKypwiS7CaAJAuiJA95yEMm/zTsm2IZzDLfPqxTS2J7HUpbVZuepV01rQuzzLPP7SSWU5NjW7/3YTvQVllC5rOI9dA3P8qLzr2zd1hNjpmzm2CS7In3uY7NzbJj1W8b+0PlMcZ5iSl7Hz77vu2S0Szb5D7YkgcEEEgSAvW0AgRGJpDd4Z1kd3Dnpcru+k222mqr/O++DWRPACTZ47JJ1o1Lkj1VkWRdqUwOYuvymXW/kGTdziTZXaGT0RQw1gFr9v6Fusn4DQEEEEAAAQQQQAABBBBAAAEEEEAAgV4KEKjvZbWQKQRmE7jxxhsnd+rfcsstkxnoyYWrrrqqlTs3ZstR81TZCwoT+67zzTffPMle3lk7Ydb/Y5I9NpiPs9566yVZVy753wwggAACCCCAAAIIIIAAAggggAACCCAwJAEC9UOqLfKKQI2AgvPbbbdd8q1vfSsf67DDDksOOOCA/O8+Dqj3rWWWWSbJ+kecZE9dtXzpS19Ksr6BS7OrLm6e8IQnJDfddFP++/77758cccQR+d8MIIAAAggggAACCCCAAAIIIIAAAgggMCQBAvVDqi3yioAlkL0gNtlmm22S1VdfffLegR/96EfJn//853wMBb/Vrcxyyy2Xf9fXAbe/+Tve8Y5J9kKbZNddd528T0HB/GuuuSY5+eSTk+OOOy7JXsKVF+Ve97rXpG/47MWp+XcMIIAAAggggAACCCCAAAIIIIAAAgggMCQBAvVDqi3yioAloJfzrrDCCtY3xcGjjjoq2XfffYtf9vQvvXBogw02yPuc982mXsT7uc99Ltlhhx18J2E8BBBAAAEEEEAAAQQQQAABBBBAAAEEeidAoL53VUKGEPAXuMc97pF3GWNP9YxnPCM55ZRTEr1kdSjp+9//frLTTjslV199tVeWV1111eSEE05I1Kc9CQEEEEAAAQQQQAABBBBAAAEEEEAAgSELEKgfcu2R9yVeYIsttkjOOeeciYP6dt9www0TBen322+/RN3HDC3dfvvtk+D70UcfnVxyySVTFyFUxo022ih54QtfOAnqL7vsskMrIvlFAAEEEEAAAQQQQAABBBBAAAEEEEBgSoBA/RQJXyAwLAG9VPXSSy9N1l577WT55ZcfVuYbcqt++HWHvfqo1x30K6+8cqLubkgIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOA7v3GDgAABfhJREFUAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJgED9mGqTsiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggMToBA/eCqjAwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIjEmAQP2YapOyIIAAAggggAACCCCAAAIIIIAAAggggAACCAxOgED94KqMDCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMSYBA/Zhqk7IggAACCCCAAAIIIIAAAggggAACCCCAAAIIDE6AQP3gqowMI4AAAggggAACCCCAAAIIIIAAAggggAACCIxJ4H8B3n9KznzdEqoAAAAASUVORK5CYII=
Test document

The code below uses Amazon Textract to extract text from the image above. Before running the code, replace <access_key_id> and <secret_access_key> on lines 5–6 with your AWS access_key_id and secret_access_key, respectively. If you don’t have these keys, follow the steps in this documentation to generate the keys.

Note: The IAM user whose credentials are being used must have the permissions to perform all the required actions.

Once you've entered the required keys, click the "Run" button to execute the code. We'll get the text in our image as the output.

import boto3
try:
# Configure AWS credentials
access_key_id = '<access_key_id>' #Enter access key here
secret_access_key = '<secret_access_key>' # Enter secret key here
session = boto3.Session(
aws_access_key_id=access_key_id,
aws_secret_access_key=secret_access_key
)
# Configure the Amazon Textract client
textract = session.client('textract', region_name='us-east-1')
# Specify the path of the document and read its contents
with open('../Doc.png', 'rb') as document:
document_contents = document.read()
# Use Amazon Textract to extract text from the document contents
response = textract.detect_document_text(Document={'Bytes': document_contents})
# Print the extracted text
for item in response['Blocks']:
if item['BlockType'] == 'LINE':
print(item['Text'])
# Exceptions handling
except Exception as e:
print(f"Error: {e}")

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