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digit classification

The goal was to create a nerual network from scratch that could classify a digit from an image. It is based on a paper I wrote in in 10th grade. I do not intend to maintain this project further.

Development

This project uses the build-tool Maven to manage the one dependency. I tested it on Java 17 although it should work on all versions > 1.7. By default, the program includes a default network that worked quite well for me.

Dataset

The program uses the csv format for its datasets, the first value represents the label of the following 784 grayscale pixels. In order to train the current nerual network I used the MNIST-Dataset as well as a self created one which turned out to work better. This might be caused by the fact, that the handwritten digits look quite diffrent from the mouse-drawn ones that this program creates.

About

a Java project to classify images of digits

Topics

digitrecognitionmachine-learning

Resources

Readme
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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digit classification
The goal was to create a nerual network from scratch that could classify a digit from an image. It is based on a paper I wrote in in 10th grade. I do not intend to maintain this project further.
Development
This project uses the build-tool Maven to manage the one dependency. I tested it on Java 17 although it should work on all versions > 1.7.
By default, the program includes a default network that worked quite well for me.
Dataset
The program uses the csv format for its datasets, the first value represents the label of the following 784 grayscale pixels. In order to train the current nerual network I used the MNIST-Dataset as well as a self created one which turned out to work better.
This might be caused by the fact, that the handwritten digits look quite diffrent from the mouse-drawn ones that this program creates.
About
a Java project to classify images of digits
Topics
digitrecognitionmachine-learning
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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pom.xml
Repository files navigation
digit classification
The goal was to create a nerual network from scratch that could classify a digit from an image. It is based on a paper I wrote in in 10th grade. I do not intend to maintain this project further.
Development
This project uses the build-tool Maven to manage the one dependency. I tested it on Java 17 although it should work on all versions > 1.7.
By default, the program includes a default network that worked quite well for me.
Dataset
The program uses the csv format for its datasets, the first value represents the label of the following 784 grayscale pixels. In order to train the current nerual network I used the MNIST-Dataset as well as a self created one which turned out to work better.
This might be caused by the fact, that the handwritten digits look quite diffrent from the mouse-drawn ones that this program creates.
About
a Java project to classify images of digits
Topics
digitrecognitionmachine-learning
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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pom.xml
Repository files navigation
digit classification
The goal was to create a nerual network from scratch that could classify a digit from an image. It is based on a paper I wrote in in 10th grade. I do not intend to maintain this project further.
Development
This project uses the build-tool Maven to manage the one dependency. I tested it on Java 17 although it should work on all versions > 1.7.
By default, the program includes a default network that worked quite well for me.
Dataset
The program uses the csv format for its datasets, the first value represents the label of the following 784 grayscale pixels. In order to train the current nerual network I used the MNIST-Dataset as well as a self created one which turned out to work better.
This might be caused by the fact, that the handwritten digits look quite diffrent from the mouse-drawn ones that this program creates.
About
a Java project to classify images of digits
Topics
digitrecognitionmachine-learning
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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.gitignore
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README.md
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pom.xml
Repository files navigation
digit classification
The goal was to create a nerual network from scratch that could classify a digit from an image. It is based on a paper I wrote in in 10th grade. I do not intend to maintain this project further.
Development
This project uses the build-tool Maven to manage the one dependency. I tested it on Java 17 although it should work on all versions > 1.7.
By default, the program includes a default network that worked quite well for me.
Dataset
The program uses the csv format for its datasets, the first value represents the label of the following 784 grayscale pixels. In order to train the current nerual network I used the MNIST-Dataset as well as a self created one which turned out to work better.
This might be caused by the fact, that the handwritten digits look quite diffrent from the mouse-drawn ones that this program creates.
About
a Java project to classify images of digits
Topics
digitrecognitionmachine-learning
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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.gitignore
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README.md
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pom.xml
Repository files navigation
digit classification
The goal was to create a nerual network from scratch that could classify a digit from an image. It is based on a paper I wrote in in 10th grade. I do not intend to maintain this project further.
Development
This project uses the build-tool Maven to manage the one dependency. I tested it on Java 17 although it should work on all versions > 1.7.
By default, the program includes a default network that worked quite well for me.
Dataset
The program uses the csv format for its datasets, the first value represents the label of the following 784 grayscale pixels. In order to train the current nerual network I used the MNIST-Dataset as well as a self created one which turned out to work better.
This might be caused by the fact, that the handwritten digits look quite diffrent from the mouse-drawn ones that this program creates.
About
a Java project to classify images of digits
Topics
digitrecognitionmachine-learning
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
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pom.xml
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digit classification
The goal was to create a nerual network from scratch that could classify a digit from an image. It is based on a paper I wrote in in 10th grade. I do not intend to maintain this project further.
Development
This project uses the build-tool Maven to manage the one dependency. I tested it on Java 17 although it should work on all versions > 1.7.
By default, the program includes a default network that worked quite well for me.
Dataset
The program uses the csv format for its datasets, the first value represents the label of the following 784 grayscale pixels. In order to train the current nerual network I used the MNIST-Dataset as well as a self created one which turned out to work better.
This might be caused by the fact, that the handwritten digits look quite diffrent from the mouse-drawn ones that this program creates.
About
a Java project to classify images of digits
Topics
digitrecognitionmachine-learning
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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5 Commits
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src/main
src/main
.gitignore
.gitignore
README.md
README.md
pom.xml
pom.xml
Repository files navigation
digit classification
The goal was to create a nerual network from scratch that could classify a digit from an image. It is based on a paper I wrote in in 10th grade. I do not intend to maintain this project further.
Development
This project uses the build-tool Maven to manage the one dependency. I tested it on Java 17 although it should work on all versions > 1.7.
By default, the program includes a default network that worked quite well for me.
Dataset
The program uses the csv format for its datasets, the first value represents the label of the following 784 grayscale pixels. In order to train the current nerual network I used the MNIST-Dataset as well as a self created one which turned out to work better.
This might be caused by the fact, that the handwritten digits look quite diffrent from the mouse-drawn ones that this program creates.
About
a Java project to classify images of digits
Topics
digitrecognitionmachine-learning
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages